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Sam Sharif
Senior AI Engineer specializing in machine learning, GenAI, and MLOps
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About
Built an end-to-end agentic population health strategy copilot for healthcare leadership, turning broad chronic disease questions into structured, evidence-backed strategy briefs. Stands out for combining healthcare domain knowledge with production-grade GenAI implementation, including LangGraph orchestration, Databricks/MLflow deployment, human review, and quality gates focused on citations, metrics, risks, and safety.
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Key Strengths
- Owned an end-to-end production healthcare AI copilot
- Strong RAG and agentic workflow design for grounded, citation-backed outputs
- Production-minded ML deployment and monitoring
- Built structured quality gates for LLM safety and consistency
- Healthcare domain expertise in population health and chronic disease strategy
- Pragmatic tradeoff decisions across latency, cost, quality, and safety
- Iterative improvement based on production behavior
- Designed reusable Python service modules that improved team velocity and reliability
- Designed and operated production AWS architectures combining FastAPI/ECS with Lambda, SQS, Step Functions, RDS, and S3
- Built multi-tenant SaaS backends with tenant scoping and JWT/JWKS-based authentication
- Reliability engineering: timeouts, retries with exponential backoff, idempotency keys, structured logging with correlation IDs
- Production deployment maturity: immutable Docker images, dev→stage→prod promotion, rolling/blue-green/canary releases, Lambda versioning with weighted aliases
- ETL/data platform delivery with AWS Glue (crawlers, catalog, PySpark jobs) including schema validation and data quality/reconciliation checks
- Modernized legacy business logic into Python services with parallel runs and parity validation (hash comparisons, statistical summaries, reconciliation reports)
- SQL performance optimization: reduced a dashboard query from ~12–25s to ~2–3s via predicate rewrite and composite indexing
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