AI Engineering Lead

Role Summary

We are hiring a founding AI Engineer Lead to build and scale AI capabilities from the ground up. This role will directly shape how AI is built and delivered across the organization. This is a hands-on engineering role responsible for designing, building, and deploying production AI solutions (copilots, agents, and automated workflows) that directly improve workflows, decision-making, and operational efficiency. Initial solutions will be built primarily in the Microsoft ecosystem (Azure, Copilot, and related tooling) to align to existing enterprise infrastructure. This role will also evaluate when more flexible, scalable, or custom AI systems are needed to ensure long-term flexibility and scalability.

Key Responsibilities

• Build and Deliver AI Solutions
  • Design, build, and deploy production-grade end-to-end AI solutions, including workflow automation agents, RAG pipelines, and copilots embedded in business workflows, and LLM-driven applications
  • Translate business needs into technical designs and working products to deliver usable, high-impact solutions, not just proofs of concept
  • Architect and implement AI-assisted data workflows and agentic systems
  • Build and maintain LLM-enabled services, prompt frameworks, and coding standards
  • Develop semantic/context layers ensuring AI outputs align with business logic and data models
  • Design multi-agent workflows, including human-in-the-loop controls
  • Make pragmatic tradeoffs to ship quickly while maintaining long-term sustainability
• Technical Design and Architecture
  • Create scalable patterns for prompt design and orchestration, agent-based workflows, and API integrations and data access
  • Inform architecture decisions for AI systems balancing speed, security, scalability, maintainability, and cost
  • Help establish engineering standards and best practices for applied AI across the organization
  • Establish reusable components, frameworks, and templates to accelerate AI development
  • Integrate AI automation with enterprise systems, APIs, and data platforms
  • Evaluate and recommend tooling across the stack (models, frameworks, vector stores, orchestration layers)
  • Define data requirements and, when needed, build or extend data pipelines to ensure AI systems have reliable, production-ready inputs
• Quality, Reliability, and Production Operations
  • Design and implement evaluation frameworks to define and track AI system performance, including task success, accuracy, latency, cost, and business impact; establish feedback loops to continuously improve quality, reliability, and cost-efficacy in production environments
  • Build guardrails and validation layers to reduce hallucinations, enforce structured outputs, and ensure safe system behavior
  • Establish monitoring and observability across AI systems (performance, usage, cost, latency, failure modes)
  • Implement modern engineering practices including CI/CD, versioning, rollback strategies, and automated testing
  • Ensure solutions meet security, compliance, and governance requirements in a regulated environment
• Cross-Functional Delivery and Adoption
  • Partner with business stakeholders, product leaders, and data teams to turn high-value opportunities into reliable, production-ready solutions