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Sai Krishna Sriram
Mid-level Generative AI & ML Engineer specializing in production LLM and RAG systems
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About
AI/ML engineer who shipped a production blood-test report understanding and personalized supplement recommendation product, using a LangGraph multi-agent pipeline on AWS serverless with OCR via Bedrock and RAG over vetted clinical research. Also built end-to-end recommender system pipelines at ASANTe using Airflow (ingestion, embeddings/features, training, registry, batch scoring/monitoring) with KPI reporting to Tableau, with a strong focus on safety, evaluation, and measurable reliability.
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Key Strengths
- Built and deployed a production multi-agent blood report analysis + supplement recommendation system on serverless AWS
- Designed robust orchestration for AI/ML workflows using AWS Step Functions (Distributed Map) and Apache Airflow DAGs
- Improved factual accuracy and reduced hallucinations via RAG with clinical research vectors (35% accuracy increase, 20% hallucination reduction)
- Strong approach to reliability: golden test sets, schema validation, groundedness checks, strict JSON/Pydantic, timeouts, max-step limits, fallbacks, and run auditing
- Expertise in messy real-world data standardization across heterogeneous lab report formats and inconsistent test naming
- Ability to translate clinician/product safety requirements into guardrails, rules, UX constraints, and measurable evaluation metrics
Reference Highlights
- Takes clear ownership and drives work to production readiness
- Reliable and delivers tasks on time
- Strong technical problem-solving
- Proactive in cross-team collaboration
- Handles messy, changing data effectively
- Improves system performance under high traffic
- Structured, clear communicator
- Explains complex systems in simple terms (starts with the 'why')
- Patient in answering questions and aligning stakeholders
- Effectively incorporates stakeholder feedback into product/system improvements
Sai's references speak for themselves. Let us connect you.
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