Muthyam Harish Kumar
Professional Summary
AI/ML Engineer with 2 years of experience designing, evaluating, and deploying LLM-powered retrieval-augmented systems and production ML pipelines. Evaluated large-scale LLM outputs for Meta and Google DeepMind via Turing, built LLM-as-judge evaluation harnesses and multi-modal data audits, and produced 400+ production-grade visualizations to surface training-data issues. Architected and deployed a production RAG pipeline for investor research (ingestion → embeddings → hybrid BM25+vector retrieval → generation → automated evaluation) with a FastAPI service and Streamlit frontend. Hands-on with Python, FastAPI, Pandas, embeddings (BGE/OpenAI), BM25, cross-encoder reranking, GPT-4o-mini, LLaMA 3.1 and AWS (S3/RDS/IAM/VPC). Seeking an AI/ML engineering role building LLM-powered products and scalable retrieval systems with end-to-end ownership.
Technical Skills
Work Experience
- Spearheaded Meta's factuality evaluation workstream by designing annotation guidelines and reviewing LLM-generated answers to reduce hallucination risk across large evaluation sets.
- Produced 400+ production-grade data visualizations for DeepMind's multimodal project using Matplotlib and Seaborn to surface labeling and training-data quality issues used by research teams.
- Designed and implemented an LLM-as-judge evaluation harness and rubric to measure faithfulness, relevance, and evasiveness, enabling repeatable automated scoring for model comparisons.
- Implemented multi-modal data quality audits for text, image, and structured inputs using Python and Pandas to identify noisy sources and improve dataset reliability.
- Automated ingestion and validation pipelines for annotation batches using Python and SQL to enforce schema checks and reduce downstream labeling errors during model evaluation.
- Built embedding and retrieval testbeds to compare vector embedding strategies and hybrid BM25+vector retrieval behaviour for downstream evaluation tasks and ablation studies.
- Led end-to-end development of a Voting Management System (VMS) and owned design, implementation, testing, and deployment to production with zero downtime.
- Implemented backend APIs and business logic in PHP and designed a normalized MySQL schema to support role-based voting workflows and audit trails.
- Developed a responsive frontend using HTML, CSS and JavaScript to provide real-time vote status and administrative controls for election officials.
- Designed and optimized SQL queries and indexes to ensure predictable response times for ballot retrieval and result aggregation.
- Deployed the application on a LAMP stack and implemented version control and deployment scripts with Git to enable safe rollbacks and releases.
- Authored unit and integration test cases and user documentation to streamline handoff and reduce operational issues during live elections.
Education
Certifications
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