Meaningful work,
meaningful life

Key Responsibilities:

• Define overall AI/ML + GenAI solution architecture for FWA detection

• Design hybrid systems combining:

  • Predictive ML models (risk scoring, anomaly detection)

  • GenAI capabilities (pattern summarization, case narratives, intelligent querying)

• Architect scalable data and ML pipelines on cloud (AWS preferred)

• Design RAG (Retrieval-Augmented Generation) frameworks

• Enable GenAI-driven investigator copilots for fraud analysis and insights

• Establish MLOps + LLMOps practices (model monitoring, prompt/version control, evaluation)

• Ensure explainability using GenAI (natural language justification of model outputs)

• Define governance, compliance, and guardrails for GenAI usage (PII, hallucination control)

• Mentor teams on best practices across ML and GenAI ecosystems

Required Skills & Experience:

• 10+ years in AI/ML with architecture experience

• Strong expertise in anomaly detection, predictive modeling, and data science frameworks

• Hands-on experience with GenAI/LLMs (prompt engineering, RAG, embeddings)

• Experience with cloud platforms like Amazon Web Services (SageMaker, Bedrock, Lambda)

• Experience designing MLOps and emerging LLMOps frameworks

• Strong understanding of healthcare claims data (ICD, CPT, billing patterns)

• Proficiency in Python, Snowflake, and SQL

• Exposure to GenAI tools like AWS Bedrock and Langchain

• Experience with Graph-based fraud detection