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Results-driven AI/ML Engineer with 6+ years of experience building production-grade Generative AI, LLM, and RAG systems across healthcare and enterprise domains. Skilled in end-to-end ML lifecycle management, including data ingestion, model development, deployment, and drift monitoring. Proven ability to deliver compliant, scalable AI solutions in regulated environments, with a focus on MLOps, orchestration, and performance/cost optimization. Experienced with LangChain, transformer fine-tuning, Spark, MLflow, and cloud platforms (AWS/GCP/Azure).…Results-driven AI/ML Engineer with 6+ years of experience building production-grade Generative AI, LLM, and RAG systems across healthcare and enterprise domains. Skilled in end-to-end ML lifecycle management, including data ingestion, model development, deployment, and drift monitoring. Proven ability to deliver compliant, scalable AI solutions in regulated environments, with a focus on MLOps, orchestration, and performance/cost optimization. Experienced with LangChain, transformer fine-tuning, Spark, MLflow, and cloud platforms (AWS/GCP/Azure).

Harshavardhan Yellala

AI Engineer, DevOps Developer, Data Scientist, +2

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Results-driven AI/ML Engineer with 6+ years of experience building production-grade Generative AI, LLM, and RAG systems across healthcare and enterprise domains. Skilled in end-to-end ML lifecycle management, including data ingestion, model development, deployment, and drift monitoring. Proven ability to deliver compliant, scalable AI solutions in regulated environments, with a focus on MLOps, orchestration, and performance/cost optimization. Experienced with LangChain, transformer fine-tuning, Spark, MLflow, and cloud platforms (AWS/GCP/Azure).…Results-driven AI/ML Engineer with 6+ years of experience building production-grade Generative AI, LLM, and RAG systems across healthcare and enterprise domains. Skilled in end-to-end ML lifecycle management, including data ingestion, model development, deployment, and drift monitoring. Proven ability to deliver compliant, scalable AI solutions in regulated environments, with a focus on MLOps, orchestration, and performance/cost optimization. Experienced with LangChain, transformer fine-tuning, Spark, MLflow, and cloud platforms (AWS/GCP/Azure).

Available to hire

Results-driven AI/ML Engineer with 6+ years of experience building production-grade Generative AI, LLM, and RAG systems across healthcare and enterprise domains. Skilled in end-to-end ML lifecycle management, including data ingestion, model development, deployment, and drift monitoring.

Proven ability to deliver compliant, scalable AI solutions in regulated environments, with a focus on MLOps, orchestration, and performance/cost optimization. Experienced with LangChain, transformer fine-tuning, Spark, MLflow, and cloud platforms (AWS/GCP/Azure).

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Work Experience

AI/ML Engineer at United Health Group

June 1, 2024 - Present

Built HIPAA-compliant MLOps and model operations for Generative AI/LLMs, including MLflow + CloudWatch monitoring for drift and inference metrics. Optimized distributed training workflows using Databricks and Spark to reduce end-to-end model training time by 30% via parallelization and scheduling. Implemented reproducibility and compliance with MLflow data versioning for auditability. Developed near real-time Spark Structured Streaming pipelines for patient risk scoring. Architected LangChain + FAISS RAG pipelines for clinical query resolution, reducing physician retrieval time by 35%. Fine-tuned BERT/ClinicalBERT for domain NLP improvements and integrated OpenAI/Vertex AI APIs for large-scale patient record summarization and clinical note generation.

AI/ML Engineer at QueryNow

November 1, 2021 - May 1, 2024

Designed multi-tenant ML inference on AWS SageMaker with auto-scaling for concurrent serving and optimized resource utilization. Created automated data validation pipelines to improve training data quality and reduce downstream model errors by 25%. Integrated Snowflake with ML pipelines for feature storage and real-time analytics. Reduced cloud ML costs on AWS and GCP by 20% through right-sizing, spot instances, and workload scheduling. Improved observability and reliability using retry mechanisms, structured logging, and distributed monitoring. Built LangChain orchestration workflows and embedding pipelines for semantic search. Developed LlamaIndex-powered ingestion for PDFs and structured sources to support unified retrieval for multi-tenant clients.

ML Ops Engineer at Risk Span Tech

February 1, 2020 - October 1, 2021

Developed Apache NiFi + Spark ETL pipelines for scalable ingestion from structured and unstructured sources to power ML workflows. Automated batch workflows using Apache Airflow DAGs, reducing manual intervention and improving reliability. Designed reusable feature stores to standardize features across multiple production models and increase development velocity. Optimized Spark jobs and SQL to reduce distributed processing time by 30%. Deployed ML models to production using Docker and REST APIs in collaboration with platform engineering teams. Built ARIMA and LSTM forecasting models and implemented anomaly detection/data validation frameworks to improve data quality. Conducted A/B testing and statistical validation to measure model performance and business impact.