Role: Technical Product Manager
Location: San Diego, California / Washington, D.C. / San Mateo, California / Remote
Employment Type: Full-Time
Salary: $190,000–$320,000 per year
About The AES Group
The AES Group is a premier technology and engineering consulting company that has been bringing businesses and talent together for over 20 years to deliver innovative solutions that have the greatest positive impact on society. AES has helped over 40 business enterprises, including Fortune 500 companies, engage their customers, empower their employees, and transform their business operations with the power of cloud, data, AI, engineering, and other emerging technologies.
About the Role
Our client is seeking an experienced Technical Product Manager to lead product strategy and development across AI-powered platforms, developer tools, and enterprise technology solutions. This role focuses on translating strategic technology objectives into actionable roadmaps, accelerating innovation, and driving adoption of agentic AI, LLM-based applications, and internal engineering platforms. You will collaborate with engineering teams and senior leadership to define product requirements, establish measurable outcomes, prioritize initiatives, and guide products from early-stage incubation through adoption and organizational handoff.
Key Responsibilities
- Own product strategy, roadmaps, requirements, and success metrics across AI platforms and developer tools.
- Translate technology strategy into clear product specifications, milestones, KPIs, and measurable outcomes.
- Drive platform adoption by improving onboarding, reducing friction, and tracking user engagement.
- Shape product direction for LLM-powered applications, AI agents, model evaluation, fine-tuning, and deployment.
- Gather feedback from engineers and stakeholders to prioritize product improvements.
- Evaluate emerging technologies and recommend build, buy, or partner strategies.
- Prepare product briefs, strategy documents, and executive updates.
- Manage product transitions from incubation to established product and engineering teams.
Required Qualifications
- 8+ years of technical product management experience.
- Technical background in Computer Science, Engineering, or a related field, or equivalent experience.
- Ability to read code, work with APIs and command-line interfaces, and prototype using AI coding tools.
- Experience launching developer tools, internal platforms, or AI/ML products and driving adoption.
- Working knowledge of LLM-based systems, AI agents, model evaluation, context management, and fine-tuning.
- Strong KPI definition, product analytics, and performance measurement skills.
- Excellent written and verbal communication and executive stakeholder management skills.
- Ability to navigate ambiguity, manage competing priorities, and deliver within aggressive timelines.
- Willingness to travel to designated locations approximately 1–3 times per quarter.
Preferred Qualifications
- Previous software engineering experience.
- Experience in defense, aerospace, robotics, or regulated industries.
- Familiarity with CUI/ITAR requirements and restricted or disconnected deployments.
- Experience with enterprise data platforms such as Databricks and ERP integrations.
- Knowledge of safety-critical software processes, requirements traceability, verification, and validation.
- Experience taking products from concept through launch, adoption, and organizational handoff.
- Familiarity with robotics or autonomous systems.
- Own product definition, roadmap, and metrics across the office of the CTO's agentic AI portfolio: an enterprise knowledge assistant (Brain), an agent-driven engineering platform (Springboard), and enterprise AI/data modernization and app development.
- Translate CTO strategy into crisp requirements, milestones, exit criteria, and KPIs — then hold the line on them.
- Drive adoption of internal platforms: reduce onboarding friction, instrument usage, track NPS and active users, and run developer advocacy and objection handling.
- Shape product direction for foundation-model based autonomy architectures and model lifecycle tooling (evaluation, fine-tuning, verification, deployment).
- Run continuous discovery with engineers, operators, and executive stakeholders; turn findings into prioritized backlogs.
- Prioritize ruthlessly across competing initiatives with small teams and aggressive timelines.
- Analyze the agentic tooling, enterprise AI platform, and foundation model ecosystem; recommend build/buy/partner positions to leadership.
- Author strategy documents, product briefs, and executive updates that drive decisions.
- Manage transitions of maturing products from incubation to owning organizations.
- Fluid intelligence that allows one to operate effectively in ambiguous conditions while finding opportunities to drive technical efforts and force multiply.
