Gen AI/ML Engineer with 4+ Years Experience

Sairam Darapuneni

Gen AI/ML Engineer

USA Phone: 203-***-**** Email: ****************@*****.*** Linkedin SUMMARY

Experienced and impact-driven Machine Learning Engineer with 4+ years of expertise in designing, developing, and deploying AI and generative models across diverse domains. Specializing in Generative AI, including large language models (LLMs), diffusion models, GANs, and multimodal systems, with a proven track record of turning cutting-edge research into scalable, real-world solutions.

Proficient in Python, PyTorch, TensorFlow, and Hugging Face Transformers, with hands-on experience across the full ML lifecycle—from data engineering and model training to evaluation, optimization, and production deployment (cloud-native and on- prem). Deep understanding of natural language processing, deep learning, and foundation model fine-tuning using techniques like LoRA, PEFT, RLHF, and prompt engineering. Strong cross-functional collaborator, able to work closely with research, product, and engineering teams to deliver AI-driven products responsibly and efficiently. Passionate about advancing the state of generative technologies while ensuring scalability, safety, and ethical alignment.

SKILLS

● Machine Learning & AI: Supervised & Unsupervised Learning Deep Learning (CNN, RNN, Transformers) Natural Language Processing (NLP) Recommendation Systems Model Deployment & Monitoring

● Generative AI & LLMs: GPT, LLaMA LangChain RAG Prompt Engineering Embeddings Vector Databases (FAISS, Pinecone, Weaviate) Copilot Studio

● MLOps & Deployment: MLflow Weights & Biases Docker Kubernetes CI/CD FastAPI Cloud & Big Data: AWS SageMaker GCP Vertex AI Azure ML Snowflake BigQuery Apache Spark Kafka

● Visualization & Reporting: Tableau Power BI Plotly Dash Data Storytelling

● Collaboration: Git/GitHub Jira Confluence Agile/Scrum Cross-functional Stakeholder Communication

● Statistical & Analytical Techniques: Hypothesis Testing A/B Testing Regression & Classification Time Series Forecasting

Clustering Dimensionality Reduction Feature Engineering

● Programming & Frameworks: Python (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Statsmodels) R SQL (Joins, CTEs, Window Functions) APIs (REST, FastAPI, Flask)

● Data Visualization & Reporting: Tableau Power BI Looker Google Data Studio Matplotlib Seaborn Plotly/Dash Interactive Dashboards Data Storytelling

● Collaboration & Project Management: Git/GitHub Jira Confluence Agile/Scrum Business Requirement Translation Cross-functional Collaboration Technical Documentation EXPERIENCE

Senior Gen AI Engineer

Fidelity, USA July 2024 – Current

● Designed and fine-tuned Large Language Models (LLMs) including GPT, LLaMA for tasks such as summarization, Q&A, text generation, and code assistance.

● Applied prompt engineering, retrieval-augmented generation (RAG), and embeddings to optimize LLM accuracy and contextual relevance.

● Developed vector search pipelines with FAISS, Pinecone, and Weaviate for semantic retrieval and conversational AI systems.

● Built multimodal models combining NLP and computer vision for image captioning, visual Q&A, and document intelligence.

● Implemented diffusion models and GANs for generative image and video applications.

● Integrated GenAI APIs and frameworks (LangChain, Hugging Face, OpenAI) into enterprise workflows.

● Established MLOps practices for GenAI, including model tracking, evaluation, and continuous improvement with MLflow and Weights & Biases.

● Deployed scalable inference services using Docker, Kubernetes, FastAPI, ensuring performance and low latency in production.

● Built cloud-native AI pipelines with Azure ML for fine-tuning, deployment, and monitoring of generative models.

● Implemented guardrails, safety checks, and content moderation pipelines to align outputs with compliance and ethical standards.

● Designed data preprocessing and synthetic data generation pipelines to improve model robustness and reduce bias.

● Collaborated with research teams to experiment with RLHF (Reinforcement Learning with Human Feedback) and other alignment techniques.

● Partnered with business stakeholders to deliver GenAI solutions for chatbots, knowledge assistants, summarization tools, and creative content generation.

● Mentored engineers on GenAI frameworks, LLM fine-tuning, and scalable deployment practices.

● Designed and deployed custom copilots using Microsoft Copilot Studio, integrating LLMs with enterprise data sources to enable conversational AI and task automation for business users.

● Configured workflows, prompts, and connectors in Copilot Studio to build domain-specific copilots, enhancing productivity and delivering actionable insights within Microsoft 365 and Dynamics environments. AI/ML Engineer

American Express, USA October 2022 – May 2024

● Designed and deployed end-to-end ML/AI solutions from data collection to production deployment.

● Built and optimized deep learning models (CNNs, RNNs, Transformers) for NLP, computer vision, and time series use cases.

● Developed and fine-tuned LLMs and NLP pipelines for chatbots, semantic search, and text analytics.

● Implemented real-time inference services using APIs (FastAPI/Flask) and integrated with cloud-based deployment.

● Deployed scalable AI models on AWS SageMaker, integrating with S3, Lambda, API Gateway, and Redshift.

● Created data preprocessing and feature engineering pipelines to support structured and unstructured datasets.

● Applied explainability and interpretability techniques (SHAP, LIME) to ensure trust in AI models.

● Integrated vector databases for embeddings-based retrieval and recommendation systems.

● Built data pipelines and ETL workflows leveraging AWS Glue, Athena, and Step Functions for ML workflows.

● Implemented model monitoring and drift detection frameworks with SageMaker Model Monitor and CloudWatch.

● Partnered with stakeholders to identify opportunities for AI-driven automation and optimization in business processes. ML Engineer

Tata Consultancy Services (TCS), India July 2021 – June 2022

● Developed and deployed machine learning models (classification, regression, NLP, and computer vision) using Python, TensorFlow, PyTorch, and scikit-learn.

● Built end-to-end data pipelines including data cleaning, preprocessing, feature engineering, and model training, integrating with MySQL/PostgreSQL databases.

● Implemented RESTful APIs and microservices in Python and Node.js, embedding ML models into production applications.

● Designed and maintained backend systems with Django, ensuring scalability, performance, and secure integration with front-end clients.

● Created interactive front-end dashboards using React.js integrated with ML services for real-time analytics and visualization.

● Containerized ML and backend services using Docker and deployed on Azure with CI/CD pipelines.

● Built ETL workflows for structured/unstructured data leveraging Pandas, PySpark for large-scale ML training.

● Applied time series forecasting and anomaly detection models for predictive insights in operational and business data.

● Collaborated with cross-functional teams to deliver AI-powered web applications, combining full-stack engineering with ML.

● Maintained Git, Agile workflows (Jira), and automated testing for model and application reliability. EDUCATION

University of Bridgeport, USA September 2022 - May 2024 Masters in Computer Science

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