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Phani Tarun Munukuntla
Junior Machine Learning Engineer specializing in LLMs, NLP, and MLOps
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About
Developed and productionized VL-Mate, a vision-language, LLM-powered assistant aimed at helping visually impaired users understand their surroundings and query internal knowledge. Emphasizes reliability and safety via confidence thresholds, uncertainty-aware fallbacks, hallucination grounding checks, and rigorous offline + user-in-the-loop evaluation, with experience orchestrating multi-step LLM pipelines (LangChain-style and custom Python async) and deploying on containerized infrastructure.
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Key Strengths
- Built and deployed a production vision-language conversational assistant for visually impaired users (VL-Mate)
- Designs safety/reliability mechanisms for noisy real-world vision inputs (confidence thresholds, uncertainty-aware fallbacks, clarification prompts)
- Strong evaluation rigor: edge-case offline benchmarks plus real user testing with visually impaired users
- Measures and mitigates hallucinations by grounding responses in vision outputs
- End-to-end LLM workflow orchestration experience (modular multi-step pipelines; custom async orchestrators for latency/retries/failures)
- Production-minded experimentation and rollout (regression tests, shadow/canary deployments, observable stages/guardrails)
- Effective collaboration with non-technical stakeholders using business metrics (task success rate, reduced manual lookup time) and prototypes
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