AI Engineering Lead

IMA Financial Group•Denver, CO

22h•$147,000 - $220,000•Hybrid

About The Position

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. The AI Engineering Lead will own development of critical AI systems while establishing technical patterns, tools, standards, and architecture for how we apply AI in the enterprise. This role will work closely with business stakeholders, product leaders, and data teams to turn high-value opportunities into reliable, production-ready solutions. The AI Engineering Lead will write production code, deliver high-impact solutions, and define how AI gets built across the organization.

Requirements

7-10+ years in software engineering, data engineering, or AI/LLM experience
Hands-on experience building and deploying production AI systems
Hands-on experience building applications using LLMs and modern AI tooling
Experience with cloud platforms (Azure preferred), Python, APIs, containerization, and CI/CD practices
Experience building RAG pipelines, agent-based workflows, or orchestration layers
Experience with vector databases, embedding pipelines, and retrieval systems
Strong problem-solving ability and bias toward practical, efficient solutions; ability to operate in a fast-moving, ambiguous environment
Experience translating business needs into technical solutions

Nice To Haves

Experience working in the Microsoft ecosystem
Experience implementing evaluation frameworks, guardrails, and observability for AI systems
Experience working in regulated industries (insurance, financial services, healthcare)
Exposure to full-stack development (frontend + backend) for delivering usable AI experiences

Responsibilities

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
Create scalable patterns for prompt design & orchestration, agent-based workflows, and API integrations & 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
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
Partner with business leaders, operations & service teams, and product stakeholders to shape use cases and turn them into working solutions
Work closely with AI Enablement to refine workflows and improve adoption
Drive fast iteration cycles, quickly moving from idea to working solution to scaled implementation; iterate solutions based on real user feedback and usage patterns