We are looking for an AI-DLC Technical Program Manager who can drive delivery in an AI-native development environment where solutions are discovered, built, evaluated, and refined rapidly. This is not a traditional TPM, Scrum Master, or Project Manager role. The ideal candidate understands how AI fundamentally changes software delivery and can orchestrate cross-functional teams through: Define Intent Build Evaluate Refine Validate Deliver. The TPM must combine technical depth, product thinking, rapid execution, and stakeholder management to help teams move from ambiguous problems to validated outcomes in days rather than traditional multi-week cycles, where appropriate.
Roles and Responsibilities: -
Drive programs using an AI-assisted development lifecycle, not simply apply AI within traditional Agile/Scrum processes. Translate business outcomes into clear intent, constraints, success criteria, evaluation scenarios, and executable work.
Facilitate collaborative build sessions across business/domain experts, AI engineers, developers, designers, and architects. Drive rapid prototyping and short feedback loops, enabling stakeholders to evaluate working solutions early.
Ensure teams continuously build, evaluate, learn, and refine rather than waiting for formal phase completion. Understand enough architecture, application development, AI/LLMs, data, APIs, security, and cloud engineering to actively participate in technical discussions.
Identify where AI can accelerate delivery versus where engineering, security, data, compliance, and human validation remain necessary. Drive AI evaluation, acceptance criteria, quality gates, risks, dependencies, and production-readiness decisions.
Set realistic client expectations around prototype speed versus production hardening. Identify reusable AI-DLC patterns, harnesses, accelerators, and working practices that improve subsequent delivery cycles.
Track progress based on validated learning, working capabilities, and business outcomes, rather than only milestones, story points, or task completion.
Experience: - 10+ years
Location: - Chicago, IL / Edina, MN (Hybrid 3 days' Work from Office)
Tentative Duration: - 6 - 12 months
Educational Qualifications: -
Engineering Degree BE/ME/BTech/MTech/BSc/MSc.
Technical certification in multiple technologies is desirable.
Skills Required:
Strong experience managing complex software/application development programs Demonstrated experience working in an AI-assisted or AI-native delivery model.
Good technical understanding of application architecture, APIs, integrations, cloud, data, CI/CD, testing, security, and observability. Practical understanding of LLMs, Agentic AI, RAG, context engineering, AI evaluation, and AI-assisted software development.
Experience with AI development environments/tools such as Claude/Claude Code, GitHub Copilot, Cursor, OpenAI/Codex, Gemini, Azure OpenAI, or equivalent. Ability to facilitate rapid build-test-evaluate-refine cycles with engineering and business teams.
Strong ability to operate in ambiguity and make problems executable without waiting for exhaustive requirements. Strong stakeholder communication and ability to challenge unrealistic timelines or assumptions with engineering evidence.
Ability to balance speed, quality, security, risk, and production readiness. What We Are Looking for Someone who: Thinks beyond backlogs, sprints, ceremonies, and project plans. Is technically strong enough to actively shape delivery with engineers.
Brings business, engineering, and domain experts together to build and make decisions in real time. Drives rapid experimentation without losing engineering discipline.
Uses AI to compress requirements, design, development, testing, and feedback loops, not just coding. Knows when to move fast and when deeper engineering or validation is required.
Measures success through validated solutions and delivered business value. What This Role Is NOT We are not looking for candidates whose experience is primarily:
Traditional Agile/Scrum program management. Sprint planning, backlog tracking, RAID management, and status reporting.
Coordinating AI/ML teams without understanding the underlying technology. Managing conventional SDLC projects that happen to include an AI component.
Using GenAI primarily for project/productivity tasks. Driving PoCs without understanding the path to production.
