Lead AI Engineer with Retirement & Wealth Domain - Remote

Lead AI Engineer with Retirement & Wealth Domain

Boston, MA or Windsor, CT (Remote)

Type : Contract

Responsibilities:-

• Architecture & Technical Design
• Hands-On Engineering
• MLOps & Production Reliability
• Technical Leadership

Experience

•            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.

Programming & Core Engineering

•            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.

•            TypeScript / 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 TypeScript 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 TypeScript/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 TypeScript (dbt, Airflow, Prefect, or equivalent); working with structured financial data at scale; experience with Snowflake, Spark, or similar.

•            Front-end capability: React with TypeScript 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, OpenSearch); relational (PostgreSQL, SQL Server); document (MongoDB); caching (Redis).

LLM & Generative AI Engineering  —  Required

•            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 (LangGraph, LangChain, Google ADK, AutoGen, 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 human-in-the-loop escalation patterns for production AI systems handling regulated financial outputs.

•            ML frameworks: working knowledge of TensorFlow and PyTorch — sufficient to fine-tune, evaluate, and integrate transformer-based models; not required to build from scratch but must understand model mechanics to make architecture decisions.

Cloud, Infrastructure & MLOps

•            Cloud platforms: production experience on AWS, Azure, or Google Cloud Platform — AI/ML services (SageMaker, Azure ML, Vertex AI), serverless compute, managed databases, and storage.

•            Containerization and orchestration: Docker (required); Kubernetes working knowledge; experience deploying AI inference services in containerized environments with auto-scaling.

•            MLOps: experiment tracking (MLflow, Weights & Biases, or equivalent); model versioning; deployment pipelines for AI systems; CI/CD for model updates with automated quality gates.

•            Observability: logging, tracing, and metrics for AI services (Datadog, CloudWatch, OpenTelemetry, or equivalent); building dashboards and alerts for model quality, hallucination rates, and system health.

Retirement & Wealth Domain Knowledge  —  Required

Engineering in a retirement context is different from general fintech. You are building systems that touch fiduciary decisions and regulated investment outputs. You do not need to be a certified financial planner, but you must understand what you are building for.

•            Defined Contribution Mechanics: 401(k)/403(b)/457 plan structures; contribution and matching rules; the recordkeeper/TPA/plan sponsor data ecosystem; what participant data looks like, where it comes from, and what PII controls apply.

•            ERISA & Fiduciary AI Constraints: how fiduciary obligations constrain what AI systems can output autonomously vs. require human review; prohibited transaction implications for AI-generated investment guidance; audit logging requirements for ERISA-covered plan decisions.

•            2026 Model Risk Management Framework: the April 2026 interagency guidance replacing SR 11-7 — including its materiality-tiered approach, proportional controls, conceptual soundness documentation requirements, and principles-based treatment of GenAI and agentic systems; how to design AI systems that generate governance evidence as a byproduct of normal engineering operations.

•            Investment Data: fund data structures (NAV, returns, expense ratios, asset allocation); participant account and transaction data; market data feeds; familiarity with Aladdin (BlackRock), Orion, or similar platforms is a plus.

•            Privacy & Security in Financial AI: SOC 2 Type II requirements; PII handling for participant financial data; data residency and retention requirements; experience building AI systems with appropriate access controls for sensitive financial information.

Tekshapers is an equal opportunity employer and will consider all applications without regards to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.