• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks using approved agent frameworks; ensure predictable behavior through explicit tool boundaries, permissions, and safety controls. • Build and maintain RAG solutions end-to-end, including ingestion pipelines, chunking/embedding approaches, retrieval strategies, and generation prompts—optimized for relevance, latency, and reliability. • Implement retrieval evaluation and continuous improvement loops by defining measurable quality signals (e.g., retrieval relevance/coverage) and iterating based on observed results and regressions. • Integrate tools and data sources using MCP (Model Context Protocol) or equivalent standard interfaces to provide consistent, secure tool access and reduce one-off integrations. • Deploy and operate AI services on AWS (e.g., Lambda, SageMaker, ECS/EKS) with strong operational practices: logging, monitoring, alerting, and well-understood failure modes. • Engineer robust runtime behavior including error handling, retries, fallbacks, circuit breakers, and guardrails to maintain safe, reliable workflows. • Enable workflow automation and integration by identifying high-value repetitive processes and delivering AI-driven automation layers that integrate into enterprise systems and platforms. • Create and maintain technical documentation for architectures, interfaces, evaluation approaches, and operational runbooks to support maintainability and auditability. • Collaborate cross-functionally with product/business stakeholders to translate workflows into well-scoped deliverables and to validate outcomes against measurable success criteria. • Other duties as assigned.

• Help define the technical direction for applied ML and AI systems that transform complex data into customer value. • Work across product, engineering, data, and ML teams to take ambiguous 0-to-1 opportunities through model development, evaluation, productionization, experimentation, and measurable customer or business impact.

• Building high-performance API services and implementing complex RAG and Agentic AI architectures. • Designing and implementing end-to-end RAG pipelines, including retrievers, vector stores (e.g., Pinecone, Weaviate, or pgvector), and generators. • Managing orchestration, tool integration, and robust error handling for non-deterministic AI outputs. • Familiarity with evaluation frameworks (e.g., RAGAS, TruLens) to assess performance, grounding accuracy, and hallucination detection. • Iterating systems based on performance metrics and continuous improvement practices.

• Build and ship mobile features for Android and/or iOS. • Create simple mobile flows for insurance, payments, claims, renewals and financial services. • Work with product, design and backend teams to turn complex journeys into usable mobile products. • Integrate APIs and handle edge cases, loading states, errors and production issues. • Improve app performance, reliability, crash rate and user experience. • Support fast product launches, experiments and iterations across mobile journeys. • Help set high standards for mobile product quality across the app.