Generative AI Engineer

The Senior AI Engineer reports to the Lead AI Engineer and serves as their primary technical partner in designing and delivering secure, scalable AI solutions across the firm. This hands-on role owns solution design and implementation for assigned initiatives, contributes to shared Engineerure standards, and partners with Data, Platform Engineering, Cybersecurity, and product teams to move AI products reliably into production.

Key Responsibilities

• Partner with the Lead AI Engineer to develop and apply reference Engineerures, engineering patterns, and standards for generative AI, machine learning, retrieval, and agentic workflows.
• Own technical design and implementation for assigned AI products, including model selection, orchestration, context management, retrieval, tools, memory, APIs, and integrations, within the broader Engineered established by the Lead AI Engineer.
• Build and apply evaluation frameworks for quality, groundedness, safety, latency, reliability, and cost, including regression testing and production release checks.
• Implement security and governance controls for identity, access, data handling, privacy, secrets, logging, auditability, human oversight, and safe failure in partnership with Cybersecurity and the Lead AI Engineer.
• Partner with Data and Platform Engineering to build reusable services and support deployment, CI/CD, MLOps or LLMOps, observability, scaling, and production operations.
• Evaluate models, platforms, vendors, and emerging capabilities; prototype solutions and provide recommendations to inform Engineerure and build-versus-buy decisions.
• Develop critical components, review code and designs, troubleshoot complex technical issues, and improve performance, maintainability, and reliability.
• Document solution designs, Engineerure decisions, production-readiness checks, and operational handoffs in line with established standards.
• Mentor AI engineers and provide day-to-day technical guidance through design reviews, reusable components, and shared learning.
• Act as a technical resource for the team, communicate design tradeoffs and delivery risks, and support the Lead AI Engineer in coordinating technical work across initiatives.

Qualifications

• Bachelor’s or advanced degree in Computer Science, Engineering, Machine Learning, or a related field, or equivalent experience.
• 6–10+ years of software, data, ML, or AI engineering experience, including technical design and delivery of production systems.
• Strong hands-on experience with LLM APIs and models, RAG, embeddings, vector search, agents, orchestration, evaluation, and AI application security.
• Hands-on experience building and running agentic systems in production. This includes agent harnesses and SDKs (e.g., Claude Code / Claude Agent SDK, OpenAI Agents SDK, LangChain, or equivalent), multi-step tool-using agents, subagent orchestration, and context and memory management across long-running tasks.
• Experience designing and deploying Model Context Protocol (MCP) servers and tools. This includes tool schema design, authentication and authorization, read-only vs. write-scoped tools, and exposing enterprise data and systems to agents safely.
• Experience designing human-in-the-loop workflows. This includes approval gates, structured input collection, asynchronous job orchestration (start / poll status / resume), and auditable handoffs between agents and business users.
• Experience building evaluation and regression harnesses for agent outputs. This includes deterministic checks of numerical and structured results against golden baselines, in addition to LLM-based quality, groundedness, and safety evaluation.
• Strong Python and software-engineering skills, including APIs, distributed systems, cloud services, testing, CI/CD, observability, and secure development.
• Experience integrating AI with enterprise data platforms, identity systems, document stores, workflows, and business applications.
• Demonstrated ability to balance rapid experimentation with reliability, governance, maintainability, and cost.
• Demonstrated ability to independently own complex technical work, collaborate across teams, communicate design decisions, and mentor engineers.

Preferred Qualifications

• Experience with Snowflake, Azure or AWS, modern data stacks, containers, infrastructure as code, and enterprise AI platforms.
• Experience in financial services, real estate, private equity, asset management, or another regulated environment.