ai/ml engineer

Ramkiran Akula

AI/ML ENGINEER

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

Phone: 469–568–3787

Professional Summary:

AI/ML Engineer with over 5 years of experience developing end-to-end machine learning, deep learning, NLP, and AI automation solutions for enterprise environments.

Strong expertise in building scalable ML pipelines, model training workflows, and production deployments using Python, TensorFlow, Py-Torch, Scikit-Learn, and AWS services.

Hands-on experience in NLP, embeddings, LLM prompt engineering, RAG architecture, and vector search using Pinecone/FAISS. Skilled in designing data ingestion, feature engineering, data validation, and ETL pipelines to support real-time and batch ML applications.

Adept at developing microservices for AI workloads using Fast API, Docker, Kubernetes, and cloud-native deployment strategies. Experience creating forecasting, anomaly detection, clustering, and recommendation models for business optimization.

Strong ability to transform business requirements into ML architectures, ensuring measurable improvements in accuracy, efficiency, and automation. Proficient in model monitoring, drift detection, experiment tracking, and ML-Ops practices using ML-flow, DVC, Airflow, and CI/CD pipelines.

Effective collaborator experienced in working with product, data engineering, and DevOps teams to deliver reliable, production-ready AI solutions. Known for strong analytical thinking, clean coding practices, and the ability to independently deliver complex AI/ML initiatives from concept to deployment.

Experienced in building real-time data streaming and event-driven ML applications using Kafka, enabling low-latency insights and automated decision workflows.

Skilled in leveraging statistical modeling and experimentation frameworks to validate model improvements and measure business impact. Strong background in designing secure, compliant AI systems with proper governance, data quality controls, and model documentation for audit readiness.

Technical Skills:

Programming: Python, R, SQL, JavaScript

ML/DL Frameworks: TensorFlow, Keras, PyTorch, Scikit-Learn, XGBoost, LightGBM

AI & LLMs: OpenAI API, LangChain, Hugging Face Transformers, RAG, Vector Databases

Data Tools: Pandas, NumPy, Matplotlib, Seaborn, Power BI

Cloud / MLOPs: AWS (S3, EC2, Lambda, SageMaker), Docker, Git, CI/CD

Techniques: NLP, CNNs, Classification, Regression, Clustering, Time Series, Recommendation Systems

Other: Jupyter Notebook, VS Code, REST API

Work Experience:

Sr. AI/ML Engineer CVS Health Jun 2023 – Present

Responsibilities:

Designed, developed, and optimized machine learning models using Python, Scikit-learn, TensorFlow, and PyTorch for classification, regression, and forecasting applications.

Developed NLP solutions using transformer models such as BERT, RoBERTa, and GPT embeddings for sentiment analysis, topic detection, semantic search, and text classification. Automated data preprocessing, feature engineering, and model evaluation workflows.

Built ETL pipelines and feature engineering processes using Python, Pandas, NumPy, and SQL to clean, transform, and prepare large datasets for machine learning models.

Applied hyperparameter tuning techniques including Grid Search, Random Search, and Optuna to improve model performance and training efficiency.

Deployed machine learning models as RESTful APIs using FastAPI, Flask, Docker, and Kubernetes to support real-time prediction services.

Implemented Retrieval-Augmented Generation (RAG) solutions using LangChain, OpenAI APIs, transformer models, and vector databases to improve knowledge retrieval and AI-powered question-answering systems.

Fine-tuned transformer-based NLP models for text classification, document retrieval, and semantic search using Hugging Face Transformers and embedding models.

Monitored model performance by tracking accuracy, precision, recall, F1-score, and data drift, supporting model validation and continuous improvement.

Performed exploratory data analysis (EDA), feature selection, statistical analysis, and data visualization using Pandas, NumPy, Matplotlib, Seaborn, and Power BI to identify trends and improve model quality.

Built CI/CD pipelines using GitHub Actions and AWS services to automate machine learning model deployment and streamline release processes.

Developed deep learning models using CNNs, LSTMs, and Transformers for image classification, natural language processing, and time-series forecasting projects.

Worked with cross-functional teams to design, develop, test, and deploy end-to-end machine learning solutions following Agile methodologies and Git-based version control.

Environment: Python, R, SQL, Scikit-learn, TensorFlow, Keras, PyTorch, XGBoost, LightGBM, Hugging Face Transformers, LangChain, OpenAI API, FastAPI, Flask, Docker, Kubernetes, AWS (S3, EC2, Lambda, SageMaker), Azure ML Studio, Databricks, MLflow, Apache Airflow, Pandas, NumPy, Matplotlib, Seaborn, Power BI, Git, GitHub, GitLab, REST APIs, GraphQL, Kafka, Redis, PostgreSQL, MongoDB, Snowflake, Jupyter Notebook.

Machine Learning Engineer Care Source Insurance May 2022 – Jun 2023

Responsibilities:

Preprocessed structured and unstructured data by handling missing values, encoding categorical features, cleaning text data, and applying feature scaling techniques to improve model performance.

Built automated data pipelines to ingest and process data from APIs, spreadsheets, cloud storage, and relational databases for machine learning applications.

Developed end-to-end machine learning workflows covering data ingestion, feature engineering, model training, validation, deployment, and monitoring.

Implemented model explainability techniques using SHAP and LIME to improve transparency and help stakeholders interpret model predictions.

Applied cross-validation techniques, including K-Fold and Stratified K-Fold, to evaluate model performance and ensure robust generalization.

Managed experiment tracking and data versioning using MLflow and DVC to support reproducible machine learning development.

Deployed scalable machine learning solutions on AWS SageMaker, EC2, Lambda, and S3 for model training, inference, and storage.

Collaborated with product owners, business analysts, and cross-functional teams to translate business requirements into AI and machine learning solutions.

Developed anomaly detection models using Isolation Forest, Autoencoders, and DBSCAN to identify abnormal patterns and support predictive monitoring.

Created ETL processes using Python and SQL to transform raw operational data into analytics-ready datasets for reporting, dashboards, and machine learning models.

Developed NLP pipelines using TF-IDF, Word2Vec, Sentence Transformers, and Hugging Face Transformers for text preprocessing, embeddings, semantic search, and document analysis.

Built real-time data processing pipelines using Apache Kafka and Python to support streaming data ingestion and machine learning inference.

Improved model inference performance through optimization techniques including model compression, quantization, and batch processing.

Built forecasting models using ARIMA, Prophet, and LSTM algorithms to predict business metrics, demand trends, and time-series patterns.

Developed recommendation systems using collaborative filtering and similarity-based algorithms to deliver personalized recommendations and improve user engagement.

Documented machine learning workflows, model architectures, deployment processes, and data pipelines to support knowledge sharing, maintenance, and audit requirements.

Environment: Python, SQL, Scikit-learn, TensorFlow, Keras, PyTorch, XGBoost, LightGBM, Pandas, NumPy, MLflow, DVC, SHAP, LIME, Hugging Face Transformers, Sentence Transformers, Word2Vec, TF-IDF, FastAPI, Flask, Apache Kafka, Apache Airflow, AWS (SageMaker, EC2, Lambda, S3), Git, Docker, REST APIs.

AI/ML Engineer E-Kalasaala Jan 2020 – Dec 2021

Responsibilities:

Designed and deployed AI-powered microservices using FastAPI, Docker, and AWS to support scalable real-time machine learning inference.

Developed NLP pipelines using transformer models including BERT, RoBERTa, and DistilBERT for document summarization, text classification, information extraction, and healthcare data analysis.

Built semantic search and information retrieval solutions using Sentence Transformers, embeddings, and FAISS to enable efficient similarity search across large datasets.

Integrated machine learning inference services with backend applications through RESTful and GraphQL APIs, supporting real-time predictions and business workflows.

Evaluated machine learning and transformer models by comparing accuracy, latency, throughput, and resource utilization to identify the best-performing models for production environments.

Developed reusable feature engineering pipelines and feature repositories to improve consistency and efficiency across machine learning projects.

Assisted in designing and executing A/B testing experiments to evaluate model performance and validate improvements before production deployment.

Implemented data quality, model monitoring, and drift detection using statistical techniques such as Kolmogorov-Smirnov (KS) Test and Population Stability Index (PSI) to maintain model reliability.

Collaborated with DevOps teams to automate model deployment using GitHub Actions, Docker, CI/CD pipelines, and AWS services, improving deployment efficiency and release quality.

Automated batch processing, model retraining, and scheduled inference workflows using Apache Airflow to support scalable machine learning operations.

Environment: Python, FastAPI, REST APIs, GraphQL, Scikit-learn, PyTorch, Hugging Face Transformers, Sentence Transformers, FAISS, Docker, AWS (Lambda, S3), GitHub Actions, CI/CD, Apache Airflow, Redis, PostgreSQL, JSON, Jupyter Notebook.

Data Science & Analytics Engineer Intern BHEL (Bharat Heavy Electricals Limited) Jun 2019 – Dec 2019

Responsibilities:

Assisted in developing AI-driven data applications and intelligent analytics platforms, improving data accessibility and enabling data-driven decision-making across business teams.

Built and maintained scalable data pipelines using Python, Java (Spring), SQL, and Oracle Database to support machine learning workflows and analytics solutions.

Performed data extraction, transformation, and loading (ETL) from multiple structured and semi-structured data sources, creating high-quality datasets for predictive modeling and reporting.

Conducted comprehensive exploratory data analysis (EDA) using statistical techniques to identify business trends, correlations, anomalies, and actionable insights.

Designed feature engineering pipelines by creating aggregated metrics, derived features, and time-based variables to improve machine learning model performance.

Assisted in developing forecasting dashboards using time-series analysis to support operational planning and business performance monitoring.

Built customer segmentation models using clustering algorithms such as K-Means and Hierarchical Clustering to analyze user behavior and support targeted business strategies.

Implemented automated data validation, anomaly detection, and quality monitoring using statistical methods and rolling-window analytics to improve data reliability.

Supported hypothesis testing, including A/B testing and statistical significance analysis, to evaluate business experiments and validate data-driven decisions.

Optimized SQL queries and database operations to improve data retrieval performance for analytical workloads and machine learning pipelines.

Collaborated with cross-functional teams to translate business requirements into AI/ML-driven analytical solutions, supporting dashboard development and predictive analytics initiatives.

Assisted in deploying, testing, and monitoring machine learning data pipelines, ensuring model input consistency, production reliability, and minimal downtime.

Gained exposure to data governance, access control, and data security best practices, ensuring secure handling of enterprise data used for analytics and machine learning.

Automated business metric calculations and reporting using Python, integrating analytical outputs with BI dashboards to provide real-time performance insights.

Environment: Python, SQL, Java (Spring), Oracle Database, Pandas, NumPy, Scikit-learn, Time-Series Analysis, Feature Engineering, Clustering (K-Means, Hierarchical), Exploratory Data Analysis (EDA), A/B Testing, ETL Pipelines, BI Dashboards, Statistical Analysis.

Education:

MS, Computer Science and Information University of North Texas, TX (May 2024)

Bachelor of Technology, Electronics & Computer Science Engineering – (Jun 2021)

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