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B Sri Charan - Senior AI ML Engineer

[email protected]

Location: Buffalo, New York, USA

Relocation: Open to relocation

Visa: H1B

Resume file: BSC_AIML_Resume_1782136582100.docx Please check the file(s) for viruses. Files are checked manually and then made available for download.

NOT A HIRING POST - RESUME FOR SENIOR AI ML ENGINEER ### Skills Summary **Senior AI/ML Engineer | GenAI | Agentic AI | LLM Specialist | 12+ Years Experience** * Generative AI: GPT-4, Claude, Gemini, Llama 3, OpenAI, LangChain, LangGraph, AutoGen, CrewAI * Agentic AI: Multi-Agent Systems, MCP (Model Context Protocol), Autonomous AI Agents, Workflow Orchestration * RAG & Vector Search: Pinecone, FAISS, ChromaDB, pgvector, Embeddings, Semantic Search, Reranking * LLM Engineering: Prompt Engineering, Fine-Tuning (LoRA, QLoRA, PEFT), Evaluation Frameworks (RAGAS, LangSmith, DeepEval) * Machine Learning & Deep Learning: TensorFlow, PyTorch, Scikit-Learn, XGBoost, Hugging Face, NLP, Computer Vision * Cloud Platforms: AWS, Azure, GCP, Vertex AI, SageMaker, Bedrock * Backend Development: Python, FastAPI, Flask, Django, REST APIs, Microservices * MLOps & LLMOps: MLflow, CI/CD, Model Monitoring, Drift Detection, Experiment Tracking * Data Engineering: PySpark, Kafka, Airflow, Databricks, AWS Glue * DevOps: Docker, Kubernetes, Terraform, Jenkins, GitHub Actions * Responsible AI: Explainability (SHAP, LIME), AI Governance, Guardrails, Compliance --- ### Resume Explanation Sri Charan is a Senior AI/ML Engineer with over 12 years of IT experience and 4+ years of focused expertise in Generative AI, Agentic AI, and Enterprise AI solutions. He has extensive experience designing and deploying production-grade LLM applications using OpenAI, Claude, Gemini, and Llama models. Currently at IDB Bank, he is building Agentic AI solutions using LangGraph, LangChain, MCP, and RAG architectures to automate business processes and improve enterprise knowledge retrieval. He has hands-on experience developing AI agents, implementing evaluation frameworks, and deploying scalable AI platforms on GCP Vertex AI. Previously at Cardinal Health, he developed ML and NLP solutions on AWS SageMaker, built recommendation systems, demand forecasting models, and large-scale data pipelines using PySpark, Kafka, and Airflow. His background combines: * AI Engineering * Quantitative Problem Solving * Enterprise Architecture * MLOps/LLMOps * Cloud-Native AI Deployments * Agentic AI & RAG Solutions ### Why He Fits the Applied AI Principal Applied AI Scientist / Quant Engineer Role Strong GenAI and LLM experience Expert in Agentic AI frameworks (LangGraph, LangChain, MCP) Hands-on RAG, Vector Databases, and Knowledge Retrieval AI Evaluation and Monitoring Frameworks Enterprise AI Governance and Responsible AI Python Backend Engineering and Microservices AWS, Azure, and GCP Cloud Expertise Experience building scalable AI platforms and reusable AI frameworks Strong leadership, architecture, and cross-functional collaboration experience ### One-Line Marketing Summary **"Senior AI/ML Engineer with 12+ years of experience specializing in Generative AI, Agentic AI, RAG, LLMOps, and enterprise-scale AI platform development across AWS, Azure, and GCP, with extensive expertise in LangChain, LangGraph, MCP, OpenAI, Claude, Gemini, and production-grade AI solutions."** Keywords: continuous integration continuous deployment artificial intelligence machine learning information technology