Technical Program Manager, Smart Manufacturing AI Pathfinding & Innovation; AIPI

Position: Technical Program Manager, Smart Manufacturing AI Pathfinding & Innovation (AIPI)
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Job Summary – Technical Program Manager, Smart Manufacturing AI Pathfinding & Innovation (AIPI)
We are seeking a highly strategic and technically fluent Technical Program Manager (TPM) to join our Smart Manufacturing AI Pathfinding & Innovation (AIPI) team. In this role, you will sit at the absolute forefront of technological discovery, guiding initiatives from speculative research concepts into functional proofs-of-concept (PoCs). This team focuses explicitly on exploring unproven AI architectures, next-generation large language models (LLMs), speculative multimodal systems, and emerging agentic frameworks.

As an AIPI TPM, you will thrive in deep ambiguity. You aren't just tracking timelines; you are structuring the discovery process itself. You will bridge the gap between research, machine learning engineers, infrastructure teams, and executive leaders to systematically de-risk and validate the future of our AI technical roadmap.

This role requires a strong technical foundation and hands-on experience in managing complex AI/ML and data-driven projects. You will collaborate with cross-functional teams including data scientists, engineers, SMEs, and senior leadership to define functional objectives, translate them into actionable plans, and ensure seamless execution.

Job Responsibilities:

Drive the daily work and enable the long-term strategy of AIPI programs.

Develop and document processes and methodologies to drive consistency across AIPI initiatives.

Represent the AIPI function in cross-functional operations initiatives/working teams/meetings to further priorities, establish relationships and act as liaison between organizations.

Develop, coordinate, and sustain top-level organizational objectives with regular read-outs to senior/executive stakeholders.

Oversee all aspects of AI pathfinding projects, including resourcing and timeline.

Identify and work to close any technical gap or barrier stalling project achievement.

Design and maintain scalable project roadmaps aligned with strategic goals.

Act as a central point of contact and single source of truth for AIPI programs.

Advise leadership on technical feasibility, risk mitigation, and resource allocation to optimize outcomes.

Partner to identify technical bottlenecks, evaluate trade-offs, and decide when to pivot, scale, or sunset experimental paths.

Remove technical and operational barriers so research teams can move fast.

Communicate high-level technical risks and breakthroughs clearly to non-technical executives.

Establish quick learning loops, milestones, and metrics for unproven technologies.

Track Objective and Program/Project Progress:

Partner closely with stakeholders and SMEs to develop meaningful, measurable objectives and actions

Implement and oversee key metrics to ensure deliverables are achieved and have desired impact

Provide clear data analysis to communicate project status, results and business impact

Propose and develop processes and policies to drive consistency across the organization and eliminate work for senior leadership

Minimum Qualifications:

Computer Science, Engineering or other related technical degree5+ years experience in complex program management and demonstrated success in global program execution

Expertise in program management, project management, and vendor relationship management

Extensive experience interacting with senior executives.

Strong problem-solving skills and the ability to make data-driven decisions in a fast-paced, dynamic environment.

Proven ability to manage complex technical programs, coordinate cross-functional teams, and drive projects to successful completion while balancing competing priorities and constraints.

Ability to navigate ambiguity, and shape unstructured, exploratory ideas into defined experimental roadmaps.

AI Literacy:
Solid foundational understanding of AI concepts, including deep learning, neural network architectures, transformers, training vs. inference life cycles, and modern data processing workflows.

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