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AI/ML Engineer with 4+ years of experience architecting and deploying production-grade ML and Generative AI solutions across healthcare, retail, and media domains. Expertise includes Python, LLMs, RAG pipelines, agentic workflows (LangGraph, CrewAI, MCP), multi-agent orchestration, prompt engineering, fine-tuning (LoRA/QLoRA), and MLOps. Hands-on experience with AWS (Bedrock, SageMaker), Azure ML, and GCP Vertex AI, along with strong observability and evaluation practices (LangSmith/Langfuse, token monitoring, drift detection). Background spans healthcare compliance (HIPAA/SOC2/GDPR) and enterprise automation, translating complex AI systems for CXO-level stakeholders.…AI/ML Engineer with 4+ years of experience architecting and deploying production-grade ML and Generative AI solutions across healthcare, retail, and media domains. Expertise includes Python, LLMs, RAG pipelines, agentic workflows (LangGraph, CrewAI, MCP), multi-agent orchestration, prompt engineering, fine-tuning (LoRA/QLoRA), and MLOps. Hands-on experience with AWS (Bedrock, SageMaker), Azure ML, and GCP Vertex AI, along with strong observability and evaluation practices (LangSmith/Langfuse, token monitoring, drift detection). Background spans healthcare compliance (HIPAA/SOC2/GDPR) and enterprise automation, translating complex AI systems for CXO-level stakeholders.

SAISH GOPISETTY

AI Engineer, Data Engineer, Data Scientist, +2

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AI/ML Engineer with 4+ years of experience architecting and deploying production-grade ML and Generative AI solutions across healthcare, retail, and media domains. Expertise includes Python, LLMs, RAG pipelines, agentic workflows (LangGraph, CrewAI, MCP), multi-agent orchestration, prompt engineering, fine-tuning (LoRA/QLoRA), and MLOps. Hands-on experience with AWS (Bedrock, SageMaker), Azure ML, and GCP Vertex AI, along with strong observability and evaluation practices (LangSmith/Langfuse, token monitoring, drift detection). Background spans healthcare compliance (HIPAA/SOC2/GDPR) and enterprise automation, translating complex AI systems for CXO-level stakeholders.…AI/ML Engineer with 4+ years of experience architecting and deploying production-grade ML and Generative AI solutions across healthcare, retail, and media domains. Expertise includes Python, LLMs, RAG pipelines, agentic workflows (LangGraph, CrewAI, MCP), multi-agent orchestration, prompt engineering, fine-tuning (LoRA/QLoRA), and MLOps. Hands-on experience with AWS (Bedrock, SageMaker), Azure ML, and GCP Vertex AI, along with strong observability and evaluation practices (LangSmith/Langfuse, token monitoring, drift detection). Background spans healthcare compliance (HIPAA/SOC2/GDPR) and enterprise automation, translating complex AI systems for CXO-level stakeholders.

Available to hire

AI/ML Engineer with 4+ years of experience architecting and deploying production-grade ML and Generative AI solutions across healthcare, retail, and media domains. Expertise includes Python, LLMs, RAG pipelines, agentic workflows (LangGraph, CrewAI, MCP), multi-agent orchestration, prompt engineering, fine-tuning (LoRA/QLoRA), and MLOps.

Hands-on experience with AWS (Bedrock, SageMaker), Azure ML, and GCP Vertex AI, along with strong observability and evaluation practices (LangSmith/Langfuse, token monitoring, drift detection). Background spans healthcare compliance (HIPAA/SOC2/GDPR) and enterprise automation, translating complex AI systems for CXO-level stakeholders.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Work Experience

AI/ML Engineer at Optum (Healthcare)

May 1, 2025 - Present

Designed and deployed end-to-end GenAI solutions using GPT-4, Claude, Gemini, and LLaMA-3 integrated with Azure OpenAI and CRM workflows, improving customer interaction efficiency by 35% and reducing operational costs by 20%. Architected multi-agent orchestration systems using LangGraph, AutoGen, and CrewAI with MCP-based tool routing, memory strategy, and state management for secure, context-aware healthcare automation. Built production RAG pipelines with Bedrock Titan embeddings, hybrid retrieval and semantic re-ranking (LangChain/LangGraph, ChromaDB, Pinecone) improving answer relevance by 28%. Developed custom MCP servers for governed tool access to internal databases, APIs, files, and vector stores. Implemented multimodal chatbot capabilities (text, audio, documents) integrated with enterprise CRM workflows. Automated support ticket triage using embeddings and ML classifiers, reducing manual classification workload by 40%. Added observability and evaluation using Langfuse, LangSmi

Data Scientist at Charles Schwab (Financial Services)

August 1, 2024 - April 30, 2025

Developed predictive churn models using Python and ML/statistical methods, improving retention strategies by 25% and enabling proactive engagement insights. Built scalable data pipelines with SQL and Spark, improving data availability and reducing delays by 30% for large-scale analytical workloads. Engineered time-series forecasting models (ARIMA, SARIMA, Prophet, LSTM) achieving ~95% accuracy for subscriber viewership prediction, enabling a 25% FTE reduction through ML-driven resource planning. Built and deployed churn prediction models (XGBoost, Random Forest, LightGBM) achieving 97% accuracy; used SHAP/LIME to explain churn drivers and support retention strategies. Implemented reusable feature store and end-to-end ML pipelines using Airflow and MLflow with PySpark preprocessing. Added real-time model monitoring for drift detection with Grafana and ELK Stack. Created BI dashboards (Looker, R Shiny, Tableau, Power BI) and collaborated across functions using Agile/Scrum practices.

Data Engineer at Infosys Limited

October 1, 2021 - August 31, 2023

Developed end-to-end ETL pipelines on AWS Glue to ingest and transform transactional banking data from core banking systems, payment gateways, and SWIFT feeds into an S3 data lake. Built EMR notebook workloads using PySpark for KYC processing, AML transaction scoring, and customer 360 aggregation across 20M+ accounts. Designed star-schema data marts in Amazon Redshift for retail banking, trade finance, and wealth management, supporting regulatory reporting (RBI/SEBI). Created parameterized AWS Glue workflows with dynamic job bookmarks and retry logic, including robust error handling for daily batch feeds from 15+ source systems. Implemented Apache Hudi tables for SCD Type 2 customer/account/product historical analytics on S3. Developed SQL stored procedures and views in Aurora PostgreSQL for credit risk reporting. Integrated AWS Comprehend for NLP sentiment analysis on customer feedback. Improved daily batch processing time by 45% through performance tuning (partition optimization and