AI Engineer with Retirement & financ and insurance Domain

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Required Retirement & Wealth Domain Expertise

Retirement Plans & Investment Knowledge

• 401(k), 403(b), and 457 plan structures.
• ERISA fiduciary obligations and AI governance constraints.
• SECURE 2.0 and 2026 Model Risk Management Framework familiarity.
• Investment products and participant financial data ecosystems.
• Recordkeeper and TPA operational understanding.

Job Summary

We are seeking a highly experienced Lead AI Engineer with deep expertise in Generative AI, LLM systems, and agentic AI architectures, combined with strong Retirement & Wealth Management domain knowledge. This role requires hands-on engineering leadership in designing, developing, and deploying enterprise-grade AI platforms in a regulated financial environment.

The ideal candidate will bring advanced experience building production-ready AI systems using Rust, TypeScript/Node.js, Solana-based architectures, and modern LLM ecosystems including RAG pipelines, MCP integrations, evaluation frameworks, and AI governance controls.

Key Responsibilities

Architecture & Technical Design

• Design scalable and secure AI platform architectures for retirement and wealth management applications.
• Define engineering standards, AI governance patterns, and system reliability strategies.
• Architect enterprise-grade RAG systems, agentic AI workflows, and AI safety layers.
• Design APIs and event-driven services for AI-powered financial platforms.

Hands-On Engineering

• Build production AI services using Rust, TypeScript/Node.js, and React.
• Develop and optimize LLM integrations using GPT-4o, Claude, Gemini, Bedrock, or equivalent.
• Implement vector search, embedding pipelines, hybrid retrieval, and prompt orchestration frameworks.
• Build Solana smart contracts and blockchain-integrated financial workflows.

MLOps & Production Reliability

• Implement CI/CD pipelines, observability, model monitoring, and automated evaluation systems.
• Establish guardrails, hallucination mitigation, fallback logic, and human-in-the-loop review systems.
• Build scalable containerized deployments using Docker and Kubernetes.
• Drive AI model lifecycle management and production monitoring.

Technical Leadership

• Lead engineering decisions across AI platform initiatives.
• Mentor engineering teams and establish best practices for AI engineering.
• Collaborate with product, compliance, legal, and infrastructure teams.
• Drive architecture reviews and operational readiness processes.

Required Qualifications

Experience

• 10+ years of progressive software engineering experience.
• 3+ years of hands-on production experience building LLM and agentic AI systems.
• Proven experience delivering enterprise-scale AI platforms in production.
• Experience working in regulated industries such as financial services or healthcare.
• Strong track record leading technical direction and engineering standards.

Required Technical Skills

Programming & Engineering

• Expert-level Rust development:
• Tokio
• async programming
• Axum/Actix
• memory-safe systems
• Strong TypeScript/Node.js expertise:
• REST APIs
• async services
• full-stack development
• React with TypeScript for advisor and participant-facing applications.
• Solana smart contract development using Anchor or native Solana programs.

LLM & Generative AI

• Production experience with:
• OpenAI GPT-4o
• Anthropic Claude
• Google Gemini/Gemma
• AWS Bedrock
• Advanced RAG architecture design and evaluation.
• Agentic AI frameworks:
• LangChain
• LangGraph
• CrewAI
• AutoGen
• MCP integrations
• Prompt engineering, output validation, and AI safety frameworks.
• LLM evaluation systems and hallucination testing.

Data & Infrastructure

• SQL and large-scale financial data processing.
• Experience with:
• Snowflake
• Spark
• Airflow
• dbt
• Prefect
• Vector databases:
• Pinecone
• Weaviate
• pgvector
• OpenSearch
• Cloud platforms:
• AWS
• Azure
• Google Cloud Platform
• Docker and Kubernetes deployment experience.

MLOps & Observability

• MLflow or Weights & Biases.
• Model deployment pipelines and evaluation automation.
• Logging and monitoring using:
• Datadog
• CloudWatch
• OpenTelemetry

Required Retirement & Wealth Domain Expertise

Retirement Plans & Investment Knowledge

• 401(k), 403(b), and 457 plan structures.
• ERISA fiduciary obligations and AI governance constraints.
• SECURE 2.0 and 2026 Model Risk Management Framework familiarity.
• Investment products and participant financial data ecosystems.
• Recordkeeper and TPA operational understanding.

Security & Compliance

• SOC 2 Type II environments.
• PII handling and financial data security controls.
• Audit logging and governance evidence generation.
• AI compliance in regulated financial environments.