Lead AI Engineer Retirement & Wealth Domain

Role:
Lead AI Engineer with Retirement & Wealth Domain

Location:
Boston, MA or Windsor, CT

Key Skill: AI, LLM, API, MLOps, Retirement & Wealth Domain

Experience: 10+ years

Mode of Hire:
Full Time

• Architecture & Technical Design
• MLOps & Production Reliability
• Technical Leadership

• 10+ years of progressive software engineering experience with sustained hands‑on contributions (aligned with Citi C14/SVP benchmark for this level).
• 3+ years of dedicated experience building LLM‑based systems and agentic architectures in production environments — not research or notebook work.
• Proven success architecting and delivering multiple enterprise‑scale AI solutions into production; can speak to architecture decisions, failure modes encountered, and how systems were improved post‑launch.
• Prior lead or staff‑level role: set technical direction, owned critical systems end‑to‑end, influenced engineering practices across a team.
• Experience delivering AI systems in a regulated environment (financial services, healthcare, or similar) with compliance, audit trail, and governance requirements.

• Rust (required, expert level): production systems development including memory safety, async programming with Tokio, error handling patterns, trait design, and testing — used for performance‑critical AI service layers, data pipelines, and backend infrastructure.
• Type Script / Node.js (required): production API services, async/await patterns, type‑safe API contracts, and React‑based front‑end interfaces for advisor and participant‑facing tools; full‑stack Type Script capability is expected, not optional.
• Solana / Solana programs (required): smart contract development using Anchor or native Solana program model; familiarity with Solana’s account model, transaction structure, and program‑derived addresses (PDAs) as they apply to on‑chain financial data and tokenized retirement or investment products.
• Software engineering fundamentals: system design, CI/CD pipeline ownership, testing strategy (unit, integration, contract, eval), resiliency patterns, security practices for AI services, and operational stability.
• API development: RESTful and event‑driven API design using Type Script/Node.js or Rust (Axum, Actix, or equivalent); authentication, rate limiting, versioning, and API contracts for AI services consumed by downstream systems.
• Data engineering: complex SQL proficiency; data pipeline construction in Rust or Type Script (dbt, Airflow, Prefect, or equivalent); working with structured financial data at scale; experience with Snowflake, Spark, or similar.
• Front‑end capability:
  React with Type Script to build production‑quality interfaces for advisor and participant‑facing AI tools — not a specialization, but full ownership of the UI layer is expected.
• Databases: vector databases (Pinecone, Weaviate, pgvector, Open Search); relational (Postgre
  
  SQL, SQL Server); document (Mongo
  
  DB); caching (Redis).
• Production LLM integration: hands‑on experience with OpenAI GPT‑4o, Anthropic Claude, Google Gemini/Gemma, and/or AWS Bedrock in user‑facing production applications — not just API experimentation.
• RAG system design and implementation: vector store selection and configuration, chunking and embedding strategies, hybrid search, re‑ranking, and rigorous evaluation (RAGAS, custom eval frameworks, or equivalent).
• Prompt engineering at an engineering level: system prompt design for financial services safety constraints, few‑shot construction, structured output extraction (JSON/XML), prompt version control, and regression testing.
• Agentic AI architecture: tool use and function calling; multi‑step reasoning chains; agent orchestration frameworks (Lang Graph, Lang Chain, Google ADK, Auto Gen, CrewAI, or custom implementations); MCP (Model Context Protocol) server design and integration for financial data sources.
• LLM evaluation: building eval suites for correctness, hallucination, instruction‑following, and task‑specific quality; LLM‑as‑judge patterns; adversarial robustness testing for financial advice contexts.
• Output validation and safety layers: guardrails, output parsers, confidence scoring, fallback logic, and…