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AI/ML Engineer with 3+ years of experience designing, deploying, and scaling production machine learning and generative AI solutions for enterprise applications. Experienced in building LLM-powered applications, RAG systems, agentic AI workflows, and AI microservices, along with end-to-end MLOps pipelines. Proven track record of delivering scalable AI platforms that improve automation, optimize inference performance, and accelerate production deployments using Python, FastAPI, LangChain/LangGraph, OpenAI APIs, and AWS/Azure, with Docker, Kubernetes, and MLflow for reliable operations.…AI/ML Engineer with 3+ years of experience designing, deploying, and scaling production machine learning and generative AI solutions for enterprise applications. Experienced in building LLM-powered applications, RAG systems, agentic AI workflows, and AI microservices, along with end-to-end MLOps pipelines. Proven track record of delivering scalable AI platforms that improve automation, optimize inference performance, and accelerate production deployments using Python, FastAPI, LangChain/LangGraph, OpenAI APIs, and AWS/Azure, with Docker, Kubernetes, and MLflow for reliable operations.

Saisravan Prasad Emmadi

AI Engineer, Data Scientist, Software Engineer, +2

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AI/ML Engineer with 3+ years of experience designing, deploying, and scaling production machine learning and generative AI solutions for enterprise applications. Experienced in building LLM-powered applications, RAG systems, agentic AI workflows, and AI microservices, along with end-to-end MLOps pipelines. Proven track record of delivering scalable AI platforms that improve automation, optimize inference performance, and accelerate production deployments using Python, FastAPI, LangChain/LangGraph, OpenAI APIs, and AWS/Azure, with Docker, Kubernetes, and MLflow for reliable operations.…AI/ML Engineer with 3+ years of experience designing, deploying, and scaling production machine learning and generative AI solutions for enterprise applications. Experienced in building LLM-powered applications, RAG systems, agentic AI workflows, and AI microservices, along with end-to-end MLOps pipelines. Proven track record of delivering scalable AI platforms that improve automation, optimize inference performance, and accelerate production deployments using Python, FastAPI, LangChain/LangGraph, OpenAI APIs, and AWS/Azure, with Docker, Kubernetes, and MLflow for reliable operations.

Available to hire

AI/ML Engineer with 3+ years of experience designing, deploying, and scaling production machine learning and generative AI solutions for enterprise applications. Experienced in building LLM-powered applications, RAG systems, agentic AI workflows, and AI microservices, along with end-to-end MLOps pipelines.

Proven track record of delivering scalable AI platforms that improve automation, optimize inference performance, and accelerate production deployments using Python, FastAPI, LangChain/LangGraph, OpenAI APIs, and AWS/Azure, with Docker, Kubernetes, and MLflow for reliable operations.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Work Experience

AI/ML Engineer at S&P Global

July 1, 2025 - Present

Designed and deployed production-grade agentic AI applications using LangGraph, OpenAI APIs, and LlamaIndex to automate financial research workflows and reduce analyst effort. Architected enterprise RAG platforms using LangChain, FAISS, Pinecone, and hybrid retrieval over 100K+ documents, improving answer relevance. Built LLM-powered enterprise applications with evaluation and guardrails to reduce hallucinations, and implemented FastAPI-based AI microservices deployed on Docker and Kubernetes to support 1,000+ daily requests. Created end-to-end MLOps pipelines using MLflow, Azure ML, Docker, Kubernetes, and CI/CD automation to reduce deployment cycles. Implemented monitoring, drift detection, and observability with Prometheus and Grafana. Optimized embedding generation, vector indexing, and retrieval pipelines to reduce latency and infrastructure costs.

Machine Learning Engineer at HCL Technologies

February 1, 2021 - November 1, 2023

Delivered end-to-end production machine learning pipelines covering data ingestion, feature engineering, training, validation, deployment, and monitoring using Python, SQL, Apache Spark, and AWS SageMaker. Built transformer-based NLP applications (semantic search, entity extraction, conversational/document intelligence) using Hugging Face, improving retrieval precision. Developed fraud detection, anomaly detection, and predictive analytics models to reduce manual investigation effort, and processed 10M+ records with distributed ETL and feature engineering using PySpark and Airflow. Deployed scalable inference services via FastAPI, Docker, Kubernetes, and AWS infrastructure. Implemented MLflow experiment tracking, model versioning, automated deployment pipelines, and monitoring, improving accuracy and reducing deployment time.