Applied AI Engineer

?Pramod Gangula

AI Engineer

Email: **********@*****.***

Contact: 347-***-****

LinkedIn: https://www.linkedin.com/in/pramod027g/

PROFESSIONAL SUMMARY

? Senior Applied AI Engineer with 12+ years of experience designing, developing, and deploying enterprise-scale Artificial Intelligence, Generative AI, Machine Learning, Data Engineering, Analytics, and Cloud-based solutions across healthcare, financial services, banking, retail, and enterprise data platforms.

? Extensive experience building production-grade Generative AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Prompt Engineering, Vector Databases, Semantic Search, Agentic AI workflows, and enterprise knowledge management solutions.

? Proven expertise in designing and implementing AI-powered copilots, intelligent assistants, document intelligence platforms, automated content generation systems, and conversational AI solutions that improve operational efficiency and business decision-making.

? Strong hands-on experience integrating OpenAI GPT models, LangChain frameworks, embedding models, vector search technologies, and enterprise APIs to deliver scalable AI solutions in production environments.

? Experienced in developing end-to-end Machine Learning solutions like data preparation, feature engineering, model training, validation, deployment, monitoring, retraining, and governance using MLOps best practices.

? Deep expertise in applying Machine Learning algorithms including Classification, Regression, Clustering, Recommendation Systems, Time Series Forecasting, Anomaly Detection, Risk Modeling, and Predictive Analytics to solve complex business problems.

? Strong experience developing and deploying Deep Learning solutions using TensorFlow, PyTorch, Neural Networks, Transformer Architectures, BERT, and advanced NLP models for enterprise use cases.

? Extensive experience implementing Natural Language Processing solutions including Named Entity Recognition (NER), Sentiment Analysis, Text Classification, Topic Modeling, Semantic Search, Document Summarization, and Information Extraction.

? Expertise in building scalable AI and Machine Learning platforms leveraging AWS cloud services including SageMaker, Lambda, S3, ECS, EKS, EC2, API Gateway, CloudWatch, Glue, IAM, and serverless architectures.

? Strong experience designing cloud-native AI solutions utilizing microservices, containerization, event-driven architectures, REST APIs, and distributed computing frameworks to support enterprise-scale workloads.

? Proven ability to architect Retrieval-Augmented Generation (RAG) pipelines combining structured and unstructured data sources to provide context-aware and highly accurate AI-driven responses.

? Extensive experience developing intelligent document processing solutions capable of extracting, classifying, validating, and summarizing large volumes of business documents and operational records.

? Strong background in advanced data science methodologies including exploratory data analysis, statistical modeling, hypothesis testing, predictive modeling, feature engineering, and business analytics.

? Hands-on experience leveraging Python libraries including Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, SciPy, Matplotlib, Seaborn, Hugging Face Transformers, spaCy, and NLTK for AI and analytics solutions.

? Experienced in developing recommendation engines, personalization platforms, customer segmentation frameworks, and behavioral analytics solutions that drive customer engagement and business growth.

? Extensive experience building fraud detection, risk assessment, anomaly detection, transaction monitoring, and predictive analytics solutions within highly regulated financial services environments.

? Strong knowledge of model explainability, responsible AI practices, model governance, monitoring frameworks, drift detection, and compliance requirements for enterprise AI deployments.

? Proven experience designing and optimizing data pipelines for structured, semi-structured, and unstructured data processing using SQL, Python, Spark, and cloud-native technologies.

? Extensive experience building scalable batch and real-time data processing solutions capable of supporting enterprise reporting, analytics, machine learning, and operational intelligence platforms.

? Strong expertise in SQL development, query optimization, data modeling, data warehousing, dimensional modeling, performance tuning, and enterprise reporting solutions.

? Hands-on experience with Apache Spark and distributed data processing frameworks to support large-scale analytics, machine learning, and data engineering workloads.

? Experienced in designing customer analytics, demand forecasting, inventory optimization, supply chain analytics, and operational intelligence solutions using predictive modeling techniques.

? Strong background in business intelligence, dashboard development, KPI reporting, executive reporting, and data visualization solutions that enable informed business decision-making.

? Proven ability to work across the full software development lifecycle including requirements gathering, architecture design, development, testing, deployment, monitoring, maintenance, and continuous improvement.

? Extensive experience collaborating with business stakeholders, product owners, architects, data engineers, security teams, compliance teams, and executive leadership to deliver enterprise technology solutions.

? Demonstrated success translating complex business requirements into scalable technical architectures, ensuring alignment between business objectives, operational needs, and technology investments.

? Strong experience implementing Agile and Scrum methodologies including sprint planning, backlog refinement, story estimation, daily standups, sprint reviews, retrospectives, and release planning.

? Experienced in preparing technical architecture documents, solution design documents, business requirement specifications, operational runbooks, governance artifacts, and knowledge transfer materials.

? Proven ability to lead technical discussions, mentor junior engineers, perform code reviews, establish development standards, and drive engineering best practices across cross-functional teams.

? Extensive experience supporting production environments, troubleshooting critical incidents, conducting root cause analysis, implementing corrective actions, and maintaining high availability enterprise systems.

? Strong understanding of enterprise architecture principles, scalability patterns, security best practices, cloud governance, and performance optimization strategies.

? Experienced in designing reusable frameworks, automation solutions, and self-service platforms that improve developer productivity, operational efficiency, and business agility.

? Delivered high-impact enterprise solutions in healthcare, banking, financial services, retail, and analytics domains while consistently meeting business objectives, compliance requirements, and project timelines.

? Adept at balancing technical excellence, business priorities, stakeholder expectations, and operational constraints to successfully deliver scalable, secure, and business-driven technology solutions.

TECHNICAL SKILLS

Programming Languages: Python, SQL, Scala, R, Java, C++, PySpark, Shell Scripting, Bash

Statistical Analysis & Data Science: Descriptive Statistics, Inferential Statistics, Hypothesis Testing, ANOVA, Chi-Square Testing, Correlation Analysis, Regression Analysis, Time Series Forecasting, Sampling Techniques, A/B Testing, Experimental Design, Feature Engineering, Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score, ROC-AUC, RMSE, MAE, MAPE), Statistical Modeling

Artificial Intelligence & Machine Learning: Supervised Learning, Unsupervised Learning, Reinforcement Learning, Ensemble Learning, Classification, Regression, Clustering, Recommendation Systems, Anomaly Detection, Fraud Detection, Predictive Analytics, Explainable AI (XAI), Model Interpretability, Dimensionality Reduction (PCA, SVD, t-SNE), Hyperparameter Tuning, Feature Selection

Machine Learning Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forest, XGBoost, LightGBM, CatBoost, Gradient Boosting, Support Vector Machines (SVM), Na?ve Bayes, K-Means Clustering, Hierarchical Clustering, DBSCAN, Apriori, Association Rule Mining

Generative AI & LLMs: OpenAI GPT-4/GPT-4o, Claude, Gemini, Llama 2/3, Mistral, LangChain, LangGraph, CrewAI, AutoGen, Retrieval-Augmented Generation (RAG), Agentic AI, AI Agents, Prompt Engineering, Prompt Tuning, Function Calling, Semantic Search, Embeddings, Fine-Tuning, Model Evaluation, LLMOps, Context Engineering

Natural Language Processing (NLP): Text Classification, Sentiment Analysis, Named Entity Recognition (NER), Topic Modeling, Text Summarization, Question Answering, Semantic Search, Information Extraction, Text Preprocessing, TF-IDF, Word Embeddings (Word2Vec, GloVe, FastText), Transformer Models, BERT, RoBERTa, DistilBERT, T5, GPT, spaCy, NLTK, Hugging Face Transformers

Deep Learning: Artificial Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), LSTM, GRU, Autoencoders, Attention Mechanisms, Transfer Learning, GANs, Diffusion Models, Computer Vision, Object Detection, Image Classification

Vector Databases & Knowledge Retrieval: FAISS, Pinecone, ChromaDB, Weaviate, Milvus, Qdrant, Vector Search, Hybrid Search, Knowledge Graphs

Python Libraries & Frameworks: Pandas, NumPy, SciPy, Scikit-learn, TensorFlow, Keras, PyTorch, XGBoost, LightGBM, StatsModels, Matplotlib, Seaborn, Plotly, Hugging Face, spaCy, NLTK

Big Data & Data Engineering: Apache Spark, PySpark, Databricks, Delta Lake, Apache Kafka, Apache Airflow, Snowflake, Data Warehousing, ETL/ELT Pipelines, Data Modeling, Data Lakes, Data Lakehouse Architecture, Data Governance, Data Quality, Data Integration

MLOps & LLMOps: MLflow, Kubeflow, Airflow, DVC, Model Registry, Experiment Tracking, Model Monitoring, Drift Detection, CI/CD for ML, Feature Stores, Model Deployment, LLM Evaluation Frameworks, Prompt Management, AI Governance

Cloud Platforms: AWS (SageMaker, Bedrock, EC2, S3, Lambda, Glue, EMR, Redshift, Athena, API Gateway, CloudWatch, IAM, QuickSight), Azure (Azure OpenAI, Azure AI Studio, Azure Machine Learning, Azure Databricks, Data Factory, Synapse Analytics, Key Vault, Event Hubs, ADLS Gen2), Google Cloud Platform (Vertex AI, BigQuery, Cloud Storage, Cloud Functions, Dataflow)

Databases: Snowflake, PostgreSQL, SQL Server, MySQL, Oracle, MongoDB, DynamoDB, Redis, Redshift

Visualization & BI Tools: Power BI, Tableau, QuickSight, SSRS, Excel, Matplotlib, Seaborn, Plotly, Kibana

DevOps & Containerization: Docker, Kubernetes, Terraform, GitHub Actions, Azure DevOps, Jenkins, Helm, GitOps

APIs & Microservices: REST APIs, FastAPI, Flask, Streamlit, Gradio, API Integration, Microservices Architecture

Version Control & Development Tools: Git, GitHub, GitLab, Bitbucket, VS Code, Jupyter Notebook, Google Colab, PyCharm

Methodologies: Agile, Scrum, SDLC, Sprint Planning, Backlog Grooming, Code Reviews, Technical Documentation, Production Support, Stakeholder Management, Cross-Functional Collaboration

PROFESSIONAL EXPERIENCES

Client: Oscar Health, NYC, NY Jan 2024 to Present

Role: Applied AI Engineer

Responsibilities:

? Designed and implemented enterprise-scale AI solutions on AWS to improve healthcare operations, automate decision-making workflows, and enable intelligent data-driven recommendations across clinical, claims, and member engagement platforms.

? Built end-to-end data science pipelines leveraging Python, SQL, and AWS services to process structured and unstructured healthcare data, resulting in improved analytical capabilities and operational efficiency.

? Developed predictive analytics models to identify high-risk members, forecast healthcare utilization patterns, and support proactive care management initiatives using machine learning and statistical modeling techniques.

? Performed extensive exploratory data analysis (EDA), feature engineering, and data profiling on large-scale healthcare datasets to uncover actionable business insights and improve model performance.

? Collaborated with healthcare domain experts, product managers, and business stakeholders to translate complex business requirements into scalable AI and machine learning solutions aligned with organizational objectives.

? Designed and deployed Retrieval-Augmented Generation (RAG) architectures using Large Language Models (LLMs) to enable secure, context-aware access to healthcare knowledge repositories and operational documentation.

? Built and optimized Generative AI applications leveraging OpenAI GPT models, LangChain, and vector databases to automate member support workflows and improve customer service response accuracy.

? Developed AI-powered healthcare assistants capable of answering policy-related questions, summarizing medical documentation, and providing contextual recommendations while maintaining HIPAA-compliant practices.

? Implemented prompt engineering frameworks, prompt evaluation methodologies, and response optimization strategies to improve LLM accuracy, consistency, and business relevance.

? Engineered semantic search solutions using embeddings and vector similarity search to improve information retrieval across healthcare records, policies, and knowledge management systems.

? Designed scalable document intelligence solutions that automated extraction, classification, and summarization of healthcare documents using advanced Generative AI and NLP techniques.

? Integrated LLM-based applications with AWS cloud services using secure APIs, event-driven architectures, and serverless deployment patterns to support enterprise-scale workloads.

? Fine-tuned open-source language models and evaluated model performance using business-specific datasets to improve domain adaptation and healthcare-specific response quality.

? Developed intelligent workflow automation solutions using GenAI technologies to reduce manual effort in claims processing, member communications, and healthcare operations.

? Built machine learning models for classification, regression, clustering, and anomaly detection use cases to support fraud detection, risk assessment, and operational optimization initiatives.

? Implemented automated feature engineering and model selection frameworks to accelerate machine learning experimentation and improve model development efficiency.

? Developed robust model monitoring frameworks to track prediction drift, data quality issues, and model performance degradation across production environments.

? Leveraged AWS SageMaker for machine learning model training, deployment, experimentation tracking, and scalable inference management across multiple business domains.

? Built recommendation systems utilizing behavioral analytics and machine learning algorithms to improve member engagement and personalized healthcare experiences.

? Applied Natural Language Processing (NLP) techniques including Named Entity Recognition (NER), text classification, topic modeling, sentiment analysis, and information extraction on healthcare-related datasets.

? Developed NLP pipelines using spaCy, NLTK, Transformers, and Hugging Face libraries to process large volumes of clinical notes, provider communications, and member interactions.

? Designed automated text summarization solutions to generate concise healthcare insights and executive-ready reports from complex medical and operational documentation.

? Built deep learning models using TensorFlow and PyTorch for advanced predictive analytics, sequence modeling, and text understanding use cases.

? Implemented transformer-based architectures including BERT and GPT-based models to improve language understanding, document processing, and conversational AI capabilities.

? Developed scalable Python-based AI applications leveraging Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, XGBoost, and MLflow for production-grade model development.

? Utilized advanced data visualization and analytics tools to communicate model outcomes, business impact, and key insights to technical and non-technical stakeholders.

? Developed REST APIs and microservices using Python frameworks to expose machine learning and Generative AI capabilities to enterprise applications and healthcare platforms.

? Implemented CI/CD pipelines for AI and machine learning workloads using AWS-native services and DevOps best practices to accelerate deployment cycles and improve release quality.

? Automated model deployment, testing, monitoring, and retraining workflows using MLOps principles to improve reliability, scalability, and operational efficiency.

? Worked extensively with cloud-native architectures on AWS, leveraging services such as SageMaker, Lambda, S3, ECS, EKS, API Gateway, CloudWatch, and IAM to deploy scalable AI solutions.

? Partnered with data engineers, software engineers, architects, compliance teams, and business stakeholders to deliver cross-functional AI initiatives within aggressive delivery timelines.

? Participated in Agile Scrum ceremonies including sprint planning, backlog grooming, daily standups, sprint reviews, retrospectives, and release planning activities.

? Collaborated closely with product owners and business leaders to prioritize AI use cases, define success metrics, and ensure alignment between technical deliverables and business objectives.

? Authored detailed technical design documents, architecture diagrams, model documentation, operational runbooks, and knowledge transfer materials to support enterprise governance standards.

? Conducted code reviews, technical mentoring sessions, and AI best-practice workshops to promote engineering excellence and knowledge sharing across teams.

? Supported production incidents, performed root cause analysis, implemented corrective actions, and continuously optimized AI systems to meet enterprise reliability and performance expectations.

Environment: Python, Generative AI, Large Language Models (LLMs), OpenAI GPT, AWS SageMaker, AWS Lambda, AWS S3, AWS ECS, AWS EKS, AWS API Gateway, AWS CloudWatch, AWS IAM, LangChain, Retrieval-Augmented Generation (RAG), Vector Databases, Machine Learning, Deep Learning, NLP, TensorFlow, PyTorch, Hugging Face Transformers, BERT, Scikit-learn, XGBoost, MLflow, Pandas, NumPy, spaCy, NLTK, SQL, REST APIs, Microservices, Semantic Search, Prompt Engineering, Model Fine-Tuning, MLOps, CI/CD, Docker, Kubernetes, Git, Agile, Scrum, Sprint Planning, Jira, Confluence, Data Visualization, Statistical Modeling, Feature Engineering, Model Monitoring, Healthcare Analytics, HIPAA Compliance.

Client: Amex, Phoenix, AZ. Nov 2020 to Dec 2023

Role: AI Engineer

Responsibilities:

? Designed and deployed enterprise-scale AI solutions on AWS to support customer intelligence, fraud detection, risk assessment, and personalized financial services across multiple business units.

? Developed advanced data science models utilizing large-scale transactional, customer behavior, and financial datasets to generate actionable business insights and improve decision-making processes.

? Performed exploratory data analysis, statistical analysis, feature engineering, and data validation on billions of transaction records to improve model accuracy and business outcomes.

? Built predictive analytics solutions to forecast customer spending patterns, credit utilization trends, customer retention, and account engagement metrics.

? Developed customer segmentation frameworks using clustering algorithms to identify behavioral patterns and enable targeted marketing and personalized customer experiences.

? Collaborated with business analysts and product teams to translate complex financial business requirements into scalable AI and machine learning solutions.

? Built machine learning models for fraud detection, transaction risk scoring, anomaly detection, and suspicious activity identification using supervised and unsupervised learning techniques.

? Designed and implemented real-time fraud detection solutions capable of processing high-volume transaction streams while maintaining low-latency decision-making capabilities.

? Developed recommendation engines leveraging customer transaction history and behavioral analytics to improve product cross-sell and customer engagement initiatives.

? Created propensity models to identify potential customers for premium financial products, resulting in improved marketing conversion rates and customer acquisition strategies.

? Implemented Natural Language Processing (NLP) solutions to analyze customer service interactions, support tickets, feedback surveys, and communication records.

? Developed text classification, sentiment analysis, topic modeling, and information extraction solutions using NLP techniques to improve customer experience initiatives.

? Built intelligent document processing solutions to automate extraction and classification of financial documents, reducing manual effort and improving operational efficiency.

? Leveraged transformer-based NLP models including BERT and domain-specific language models to improve language understanding and text analytics capabilities.

? Developed machine learning pipelines using Python and Scikit-learn for automated model training, validation, hyperparameter tuning, and performance evaluation.

? Built deep learning models using TensorFlow and PyTorch for customer behavior prediction, fraud analytics, and advanced pattern recognition use cases.

? Implemented neural network architectures to improve prediction accuracy across customer analytics and financial risk management initiatives.

? Designed automated feature engineering frameworks to improve model development efficiency and accelerate experimentation cycles.

? Utilized Python libraries including Pandas, NumPy, SciPy, Matplotlib, Seaborn, Scikit-learn, TensorFlow, and PyTorch for advanced analytics and model development.

? Developed scalable data processing workflows using AWS services to support machine learning model development, training, and production deployment activities.

? Leveraged AWS SageMaker for model training, experimentation, deployment, and lifecycle management across multiple AI initiatives.

? Built and deployed REST APIs and microservices to expose machine learning predictions and AI capabilities to internal enterprise applications.

? Implemented MLOps best practices including automated model deployment, version control, monitoring, retraining, and CI/CD integration.

? Developed model monitoring solutions to track prediction quality, model drift, data drift, and production performance metrics.

? Worked extensively with AWS cloud services including SageMaker, S3, Lambda, EC2, ECS, CloudWatch, IAM, Glue, and API Gateway to build scalable AI platforms.

? Optimized machine learning workflows and cloud resource utilization, resulting in reduced infrastructure costs and improved model execution performance.

? Partnered closely with data engineers, software developers, architects, cybersecurity teams, and business stakeholders to deliver enterprise AI solutions.

? Participated in Agile Scrum ceremonies including sprint planning, daily standups, backlog refinement, sprint reviews, retrospectives, and release planning sessions.

? Prepared detailed technical documentation, solution architecture documents, model governance artifacts, operational runbooks, and knowledge transfer materials.

? Supported production AI applications, conducted root cause analysis, resolved critical incidents, mentored junior team members, and contributed to continuous improvement initiatives across multiple cross-functional teams.

Environment: Python, Machine Learning, Artificial Intelligence, AWS SageMaker, AWS S3, AWS Lambda, AWS EC2, AWS ECS, AWS Glue, AWS CloudWatch, AWS IAM, SQL, Scikit-learn, TensorFlow, PyTorch, NLP, BERT, Deep Learning, Fraud Detection Models, Predictive Analytics, Recommendation Systems, Customer Analytics, Statistical Modeling, Feature Engineering, Pandas, NumPy, SciPy, Matplotlib, Seaborn, REST APIs, Microservices, MLOps, CI/CD, Model Monitoring, Docker, Git, Agile, Scrum, Jira, Confluence, Data Visualization, Financial Analytics, Risk Modeling, Anomaly Detection, Model Governance.

Client: HEB, Austin, TX Sep 2017 to Oct 2020

Role: ML Engineer

Responsibilities:

? Developed and deployed machine learning solutions to support retail operations, customer analytics, demand forecasting, inventory optimization, and supply chain decision-making across HEB's enterprise data platform.

? Built predictive models using Python and machine learning algorithms to forecast product demand across stores, improving inventory planning and reducing stockout occurrences.

? Performed extensive data cleansing, feature engineering, exploratory data analysis, and statistical modeling on large-scale retail transaction and customer datasets.

? Developed customer segmentation models using clustering techniques to identify purchasing behaviors and support personalized marketing campaigns.

? Built recommendation systems leveraging customer purchase history and behavioral analytics to improve product recommendations and increase customer engagement.

? Designed machine learning models for sales forecasting, inventory replenishment planning, and seasonal demand prediction across multiple retail categories.

? Collaborated with merchandising, supply chain, marketing, and business teams to understand operational challenges and translate business requirements into data-driven solutions.

? Developed classification and regression models to improve pricing strategies, customer retention initiatives, and promotional campaign effectiveness.

? Built anomaly detection models to identify unusual sales patterns, inventory discrepancies, and operational inefficiencies across retail locations.

? Developed scalable data processing pipelines using Apache Spark and Python to process large volumes of retail and operational data efficiently.

? Utilized machine learning algorithms including Random Forest, XGBoost, Logistic Regression, Decision Trees, Gradient Boosting, and Clustering techniques to solve complex business problems.

? Leveraged AWS cloud services including EC2, S3, EMR, Lambda, and RDS to support machine learning model development, training, and deployment activities.

? Developed automated model training and evaluation pipelines to improve machine learning lifecycle management and reduce manual intervention.

? Built data visualization dashboards and analytical reports to communicate business insights, model performance, and key operational metrics to leadership teams.

? Worked extensively with Python libraries including Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, and Seaborn for machine learning and advanced analytics.

? Developed REST-based services and batch processing frameworks to operationalize machine learning models and integrate predictions into downstream business applications.

? Participated in Agile Scrum ceremonies including sprint planning, daily standups, backlog grooming, sprint reviews, retrospectives, and release planning activities.

? Authored technical documentation, solution design documents, data dictionaries, model validation reports, and operational support guides to support governance and knowledge transfer.

? Collaborated with cross-functional teams including data engineers, software developers, QA teams, product owners, and business stakeholders to deliver high-quality machine learning solutions.

? Provided production support, monitored model performance, conducted root cause analysis, and implemented continuous improvements to ensure reliable and scalable machine learning operations.

Environment: Python, Machine Learning, Apache Spark, AWS EC2, AWS S3, AWS EMR, AWS Lambda, AWS RDS, SQL, Scikit-learn, XGBoost, Random Forest, Decision Trees, Logistic Regression, Gradient Boosting, Clustering, Predictive Analytics, Demand Forecasting, Inventory Optimization, Recommendation Systems, Customer Analytics, Anomaly Detection, Statistical Modeling, Feature Engineering, Pandas, NumPy, SciPy, Matplotlib, Seaborn, REST APIs, Data Visualization, Agile, Scrum, Jira, Confluence, Git, Retail Analytics, Supply Chain Analytics, Production Support, Model Deployment..

Client: JPMC, Dallas, TX Sept 2013 to Aug 2017

Role: Data Analyst

Responsibilities:

? Gathered, analyzed, and interpreted large volumes of financial, customer, and operational data to support business decision-making, regulatory reporting, and strategic initiatives across banking operations.

? Developed complex SQL queries, stored procedures, views, and data extraction processes to generate business-critical reports and analytical datasets for multiple business units.

? Created and maintained enterprise reporting solutions using SQL Server Reporting Services (SSRS), enabling leadership teams to monitor key performance indicators and operational metrics.

? Designed interactive dashboards and visual reports using Tableau and Excel to provide actionable insights into customer behavior, financial performance, and operational efficiency.

? Performed data profiling, data validation, and reconciliation activities to ensure accuracy, completeness, and consistency of data across multiple source systems.

? Worked extensively with data warehouse environments to extract, transform, and analyze large-scale financial datasets for business intelligence and reporting purposes.

? Assisted in the development and enhancement of ETL processes using SQL Server Integration Services (SSIS) to support data integration and reporting requirements.

? Conducted trend analysis, variance analysis, and ad-hoc reporting to identify business opportunities, operational risks, and performance improvement areas.

? Collaborated with business stakeholders, product owners, and subject matter experts to gather reporting requirements and translate business needs into technical solutions.

? Supported regulatory and compliance reporting initiatives by validating financial data, preparing audit-ready reports, and ensuring adherence to banking standards.

? Developed automated reporting solutions that reduced manual reporting efforts and improved data delivery timelines across various business functions.

? Participated in Agile Scrum activities including requirement gathering sessions, sprint planning, daily standups, user acceptance testing, and release validation processes.

? Created detailed business requirement documents, functional specifications, data mapping documents, and reporting documentation to support project delivery and knowledge transfer.

? Worked closely with data engineers, database administrators, QA teams, and business users to troubleshoot data issues, perform root cause analysis, and ensure successful project implementation.

? Provided production support for reporting and analytical applications, monitored data quality issues, resolved reporting discrepancies, and contributed to continuous process improvement initiatives.

Environment: SQL, T-SQL, SQL Server, SSRS, SSIS, Tableau, Microsoft Excel, Data Warehousing, ETL, Data Analysis, Data Validation, Data Profiling, Data Reconciliation, Business Intelligence, Reporting, Dashboard Development, Stored Procedures, Views, Performance Tuning, Financial Analytics, Ad-hoc Reporting, Requirement Gathering, UAT Testing, Agile, Scrum, Jira, Confluence, SDLC, Production Support, Microsoft Office Suite.

CERTIFICATIONS

? AWS Certified Machine Learning Engineer ? Associate

? AWS Certified AI Practitioner

EDUCATION

? Completed Bachelor in Electronics and Communications Engineering from Sri Indu College of Engineering and Technology, Hyderabad, India

? Completed master?s in data science from University of North Texas, Denton, USA.

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