Machine Learning Engineer

• Gather and integrate large volumes of data, perform analysis, interpret results, and develop actionable insights and recommendations
• Develops and maintains infrastructure systems that connect internal data sets
• Creates new data collection frameworks for structured and unstructured data
• Leads enterprise-scale AI initiatives by designing horizontal capabilities such as RAG, evaluations-as-a-service, prompt/version control, guardrails, feature, and vector stores
• Develops, analyzes, and models complex operational, clinical, and economic data
• Deliver end-to-end ML systems (XGBoost / LightGBM) and LLM systems with clear SLOs
• Architect scalable solutions including cloud lakehouse (Databricks / Spark, SQL), streaming (Kafka/Kinesis), and API-first approaches
• Oversee serving technologies (vLLM / Triton / KServe / Ray / SageMaker)
• Establish and manage LLMOps / MLOps processes using tools like MLflow, CI/CD, and IaC
• Implement data leadership strategies including data contracts, quality SLAs, and FHIR-aware design for PHI/PII
• Develop Responsible AI frameworks including fairness/robustness evaluations, red-teaming, and model risk management, ensuring audit readiness (HIPAA, SOC 2, HITRUST)
• Define visions and OKRs for strategy and portfolio management; orchestrate build-buy-partner decisions and optimize ROI and FinOps
• Mentor principal and lead contributors, drive design reviews, and oversee Agile/SAFe delivery across multiple teams
• Influence VP/C-suite and ensure cross-functional alignment

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