AI/ML & Generative AI Engineer

Lakshmi Pulicharla

+1-314-***-**** ******************@*****.***

AWS Certified ML Associate

Profile Summary

● AI/ML Engineer with 3+ years of experience designing, developing, deploying, and operating end-to-end machine learning and Generative AI solutions across regulated financial services, healthcare, and high-volume enterprise data environments.

● Strong Python, SQL, Spark, PySpark, PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, and software- engineering foundation with experience building scalable data pipelines, REST APIs, model-training workflows, and production inference services.

● Experienced with structured and unstructured data ingestion, preprocessing, feature engineering, ETL/ELT, schema validation, batch processing, and near-real-time pipelines using Spark, PySpark, Kafka, Airflow, AWS Glue, S3, Snowflake, Pandas, and NumPy.

● Built production-oriented LLM, RAG, agentic, NLP, computer vision, classification, regression, and anomaly-detection solutions using OpenAI, Claude, Gemini, AWS Bedrock, LangChain, LangGraph, LlamaIndex, PyTorch, TensorFlow, and XGBoost.

● Hands-on experience with MLOps practices including MLflow, Docker, Kubernetes, Terraform, GitHub Actions, CI/CD, automated testing, model versioning, monitoring, drift detection, governance, and deployment documentation.

● Skilled in prompt engineering, context engineering, embeddings, vector databases, model evaluation, hyperparameter tuning, explainability, latency and cost optimization, batch and real-time inference, and production troubleshooting.

● Collaborative engineer who translates ambiguous business requirements into secure, maintainable AI applications and communicates tradeoffs in quality, scalability, reliability, explainability, compliance, and cost to technical and non-technical stakeholders.

● AWS Certified Machine Learning Engineer - Associate with a Master’s degree in Computer Science and experience across the full AI/ML lifecycle, distributed systems, cloud platforms, responsible AI, and regulated-industry delivery. TECHNICAL SKILLS

Programming & Software Engineering: Python, SQL, Java, C++, TypeScript/JavaScript, Object-Oriented Design, Data Structures, Algorithms, REST APIs, FastAPI, Flask, Streamlit, PyTest, Git, GitHub, Code Reviews Cloud & Deployment: AWS SageMaker, S3, Glue, Lambda, Bedrock, EKS, CloudWatch, API Gateway, Azure Databricks, Azure OpenAI, GCP Vertex AI Concepts, Docker, Kubernetes, Terraform, Cloud-Native Services Data Engineering & Databases: Apache Spark, PySpark, Kafka, Airflow, ETL/ELT, Data Lakes, Snowflake, SQL and NoSQL Concepts, Batch and Streaming Pipelines, Feature Engineering, Dataset Validation MLOps & Automation: MLflow, GitHub Actions, CI/CD, Reproducible Training, Experiment Tracking, Model Versioning, Automated Testing, Monitoring, Drift Detection, Retraining Support, Governance, Rollback and Release Controls Machine Learning & Deep Learning: scikit-learn, XGBoost, PyTorch, TensorFlow, Hugging Face Transformers, Pandas, NumPy, Classification, Regression, Recommendation Concepts, Anomaly Detection, NLP, Computer Vision, Hyperparameter Tuning

Generative AI & Agentic Systems: OpenAI GPT, Anthropic Claude, Gemini, Llama and Open-Source Model Concepts, LangChain, LangGraph, LlamaIndex, MCP, RAG, FAISS, ChromaDB, Embeddings, Vector Search, Prompt Engineering, Tool Calling, Structured Outputs

Reliability, Responsible AI & Optimization: Logging, Metrics, Tracing, Data and Model Drift, Latency/Error Monitoring, Root- Cause Analysis, Explainability, PII Controls, Access Controls, Auditability, Cost Optimization, Secure Data Handling Architecture & Development Practices: Distributed Systems, Microservices, Event-Driven Architecture, Full-Stack AI Applications, API Integrations, Agile/Scrum, Technical Documentation, Architecture Diagrams, Deployment Guides Production AI Delivery: Concept-to-Production Ownership, Model Packaging, Batch and Real-Time Serving, Evaluation Frameworks, Regression Testing, Performance Optimization, Monitoring, Retraining, Lineage, Compliance, Runbooks Domain Experience: Financial Services, Fraud and Credit Risk, Healthcare Analytics, Enterprise AI Applications, Transactional and Time-Series Data, Regulated Production Systems, Data Governance EXPERIENCE

Central Bank Feb 2025 – present

AI/ML Engineer St. Louis, MO, USA

● Built and operated end-to-end machine learning and Generative AI solutions for transaction monitoring, fraud investigation, risk intelligence, and regulatory reporting using Python, SQL, Spark, PySpark, SageMaker, Bedrock, S3, Glue, Lambda, and CloudWatch.

● Developed scalable ingestion, preprocessing, feature-engineering, and training pipelines that transformed high-volume structured and unstructured financial data into validated datasets for model development and inference.

● Designed and evaluated classification, anomaly-detection, and risk-scoring models using scikit-learn, XGBoost, PyTorch, and TensorFlow, applying cross-validation, threshold tuning, explainability, and error analysis.

● Built Python and FastAPI services with retries, fallbacks, idempotent processing, structured logging, validation, and secure integrations with enterprise databases and third-party APIs.

● Developed RAG and agentic applications with LangChain and LangGraph that retrieved transaction records, risk signals, and policy documents and generated grounded investigation summaries through tool-calling workflows.

● Supported SageMaker training and inference workflows, including experiment tracking, model packaging, versioning, batch transform, near-real-time endpoints, controlled deployments, and rollback-ready release practices.

● Implemented responsible AI and governance controls for sensitive financial data, including least-privilege access, encryption-aware handling, audit logging, structured output validation, human-review checkpoints, and model lineage.

● Containerized AI/ML services with Docker and automated unit and integration testing, infrastructure provisioning, packaging, and deployment through Terraform and GitHub Actions CI/CD pipelines.

● Instrumented production models and agent workflows for quality, groundedness, latency, errors, availability, data freshness, feature drift, score distributions, and failure modes; converted issues into regression tests.

● Collaborated with data scientists, software engineers, DevOps teams, compliance partners, and business stakeholders to define requirements, integrate data sources, troubleshoot production issues, and deliver maintainable AI solutions.

● Contributed across the full lifecycle from problem framing, data preparation, feature engineering, training, and evaluation through API integration, deployment, monitoring, retraining support, documentation, and incident resolution.

● Optimized pipelines and inference services for latency, throughput, scalability, reliability, and cloud cost by profiling bottlenecks, tuning workloads, and balancing batch versus real-time processing.

● Evaluated emerging LLMs, frameworks, embedding models, and deployment patterns and translated findings into practical implementation recommendations and reusable components. Environment: Python, SQL, PyTorch, TensorFlow, scikit-learn, XGBoost, AWS SageMaker, Bedrock, S3, Glue, Lambda, CloudWatch, Spark, PySpark, Kafka, FastAPI, LangChain, LangGraph, RAG, FAISS, Docker, Kubernetes, Terraform, GitHub Actions, MLflow, CI/CD

BJC Healthcare May 2024 – Jan 2025

AI/ML Engineer St. Louis, MO, USA

● Developed and productionized AI/ML applications for patient-risk prediction, readmission analysis, clinical decision support, and unstructured healthcare data using Python, scikit-learn, PyTorch, TensorFlow, Transformers, and Azure services.

● Built scalable ETL, feature-generation, and validation pipelines for structured EHR data, clinical text, images, and device-generated streams using Spark, Kafka, Pandas, NumPy, and Azure Databricks.

● Created reusable preprocessing, temporal validation, feature-engineering, and evaluation components that improved reproducibility and supported iterative machine learning and deep learning development.

● Implemented Airflow workflows for scheduled processing and retraining with dependency management, retries, alerts, failure notifications, and recoverable multi-stage execution.

● Developed NLP, document-intelligence, and computer-vision workflows using spaCy, Transformers, OpenCV, and OCR for entity extraction, classification, summarization, and structured information generation.

● Applied SHAP, PCA, statistical evaluation, hyperparameter tuning, and structured error analysis to improve model quality, interpretability, and consistency.

● Automated training, testing, experiment tracking, versioning, and deployment with MLflow, GitHub Actions, Docker, and Azure Databricks CI/CD workflows.

● Supported near-real-time ingestion with Kafka and production monitoring to maintain feature freshness, schema consistency, pipeline integrity, reliable inference, and privacy-aware handling of PHI and PII.

● Collaborated with clinicians, data scientists, engineers, and governance stakeholders to translate healthcare requirements into secure, maintainable AI applications and APIs.

● Documented architecture, data transformations, validation rules, model assumptions, evaluation results, deployment procedures, and operational runbooks to support auditability and knowledge transfer. Environment: Python, SQL, PyTorch, TensorFlow, scikit-learn, Transformers, OpenCV, OCR, MLflow, Azure Databricks, Airflow, Spark, PySpark, Kafka, FastAPI, Docker, Pandas, NumPy, CI/CD Wells Fargo Jan 2023 – Nov 2023

ML Engineer Hyderabad, India

● Developed machine learning solutions for credit risk, fraud detection, customer analytics, and predictive modeling using Python, SQL, PySpark, scikit-learn, and XGBoost.

● Converted analytical notebooks into modular, tested, maintainable Python pipelines and REST services suitable for repeatable training, enterprise integration, and production deployment.

● Built feature-engineering, label-preparation, ETL, and dataset-validation workflows over large transactional and customer datasets using SQL, PySpark, Spark, Pandas, and NumPy.

● Supported end-to-end ML workflows covering ingestion, preprocessing, model training, temporal validation, batch and near-real-time inference, API deployment, monitoring, and retraining support.

● Improved model quality through cross-validation, hyperparameter tuning, threshold optimization, evaluation metrics, explainability, and systematic error analysis.

● Developed Flask-based REST inference services and integrated models with downstream financial applications, databases, and batch-processing workflows.

● Added automated testing, source control, model versioning, CI/CD, logging, alerting, data-quality checks, and model- drift monitoring to improve operational reliability.

● Worked with software, data, platform, and risk teams to resolve production issues and meet security, scalability, explainability, lineage, governance, and audit requirements. Environment: Python, SQL, PySpark, Spark, scikit-learn, XGBoost, Flask, Pandas, NumPy, Feature Engineering, Model Training, REST APIs, CI/CD, Monitoring, Explainability

Myntra Aug 2021 – Dec 2022

Data Engineer Bengaluru, India

● Developed Python, SQL, and PySpark ETL/ELT pipelines for high-volume transactional, pricing, customer, and clickstream datasets, improving processing efficiency by 45%.

● Optimized distributed Spark transformations and resolved data-quality issues to produce reliable time-aware datasets for analytics, experimentation, and downstream ML feature generation.

● Built batch and near-real-time processing jobs for structured and semi-structured event data, supporting scalable feature pipelines and time-sensitive analytics workflows.

● Integrated AWS S3 and Snowflake data sources to support centralized analytics, feature engineering, experimentation, and model-development use cases.

● Implemented automated schema validation, reconciliation, freshness checks, and data-quality controls to improve dataset reliability, traceability, and reproducibility.

● Developed event-driven ingestion with Spark and AWS Lambda to improve the availability of operational and behavioral signals for downstream systems.

● Built pipeline monitoring and alerting with PyTest, Prometheus, and ELK to detect failures, delays, quality violations, and abnormal workload behavior quickly.

● Collaborated with data scientists and engineers to define feature requirements, onboard data sources, document schemas, and support experimentation.

● Environment: Python, PySpark, SQL, Apache Spark, AWS S3, AWS Lambda, Snowflake, GitHub Actions, PyTest, Prometheus, ELK, ETL/ELT, Distributed Data Processing, Data Quality Environment: Python, PySpark, SQL, Apache Spark, AWS S3, AWS Lambda, Snowflake, GitHub Actions, PyTest, Prometheus, ELK, Linux, Data Modeling, Distributed Systems.

EDUCATION

SouthEast Missouri State University

Master’s degree in computer science Cape Girardeau, MO, USA

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