Technical Product Manager for the AI Platform.

Position Overview: We are seeking a Technical Product Manager for the AI Platform to lead the strategic planning, technical coordination, and execution across our enterprise AI/ML and Agentic platform ecosystem. In this role, you will bridge deep engineering capability with product management rigor to convert enterprise AI strategy into actionable, dependency-aware roadmaps. You will oversee a unified platform that powers AI agents, Generative AI, traditional ML, and shared foundational capabilities.

This role requires a technical mindset with a background in software development, microservices architecture, and hands-on understanding of APIs, protocol integrations (such as Model Context Protocol - MCP), and End-to-End (E2E) testing. You will partner with Engineering Leads, AI Architects, QA, Security, Compliance, and business stakeholders to deliver reliable, secure, observable, and cost-effective AI platform products that accelerate time-to-value across the organization. What will you do?

Platform Roadmap & Strategy: Translate AI vision into a multi-quarter, dependency-aware platform roadmap; prioritize platform capabilities based on measurable business impact, cost-efficiency, technical risk, and architectural readiness.

Technical Backlog & Requirements Definition: Write high-quality Epics, User Stories, and Technical Specifications tied to clear outcomes, success metrics, and robust Definition of Ready (DoR) / Definition of Done (DoD).

Architecture & Systems Alignment: Partner with architects and engineering squads to define platform-level features around microservices, REST/gRPC APIs, backend build services, Model Context Protocol (MCP) integrations, and LLM orchestration layers.

Testing & Quality Assurance Oversight: Deeply understand and review test cases, define acceptance criteria, and ensure comprehensive End-to-End (E2E) test coverage across AI pipelines, agentic workflows, model evaluations, and integrations.





Dependency & Execution Management: Identify, map, and resolve cross-team technical dependencies across multiple engineering squads, data platforms, and external vendor/partner APIs.

Release & Lifecycle Management: Own platform release planning, environment readiness, promotion criteria, rollout/rollback strategies, and release notes across staging and production environments. Governance, Security & Compliance: Collaborate with Security, Data Governance, and Compliance teams to enforce data boundaries, access controls, auditability, and guardrail policies within the AI platform.

Observability & Cost Management: Track platform reliability (SLIs/SLOs), compute/token spend, and usage metrics using enterprise monitoring tools; drive FinOps optimizations for model inference and infrastructure.

Process Standardization: Champion best practices in Agile/Scrum, SDLC discipline, Jira workflows, and CI/CD promotion standards across AI engineering squads.

Qualifications

- Education & Experience: ~ Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, or a related technical field. ~5+ years of experience as a Technical Product Manager, Technical Product Owner, or Technical Lead for platform, infrastructure, developer tools, or distributed software systems. ~ Prior hands-on experience as a Software Engineer, Backend Developer, or AI/ML Engineer (strongly preferred).
- Software Engineering & Technical Stack: Deep understanding of the full Software Development Life Cycle (SDLC) and modern engineering workflows (CI/CD, Git, automated builds).



Knowledge and understanding of Model Context Protocol (MCP) for connecting AI models to external tools and data sources. Practical capability to read/review code, evaluate test cases, and validate End-to-End (E2E) functional and integration tests.
- AI & Machine Learning Foundations: Solid knowledge of AI/ML fundamentals, LLMs, prompt frameworks, agentic workflows, RAG architectures, vector databases, and evaluation pipelines (Evals). Familiarity with AI guardrails, model routing/gateways, token cost optimization, and inference latency management.
- Methodologies & Tools: ~ Advanced proficiency with Jira, Confluence, and Agile/Scrum frameworks.

Skills: Hard Skills Technical Product Management (Platform/APIs) AI / LLM / Agentic Systems &

• ML Foundations Model Context Protocol (MCP) &

• Agent Tooling Agile / Scrum / Backlog Refinement (Jira, Confluence) Observability &

• SLI/SLO Monitoring Soft Skills Technical Systems Thinking &

• Architectural Empathy Strategic Prioritization &

• Trade-off Analysis Cross-Functional Leadership &

• Stakeholder Management Complex Dependency &

• Risk Management Analytical &

• Data-Driven Problem Solving Clear Technical Communication (Engineering to Executive) Detail Oriented &

• Quality-Driven Execution Technical Expertise AI Platform Management Agentic Architectures &

• Generative AI Infrastructure Software Engineering &

• API Ecosystems Nice to have: Familiarity with Microservices Architecture &

• API Design (REST/gRPC) Familiarity with Cloud-Native Architecture (AWS, Docker, Kubernetes) SDLC &

• CI/CD Pipelines. Familiarity with in cloud-native environments (AWS/Azure/GCP, Docker, Kubernetes/EKS). Familiarity with telemetry and monitoring tools (Grafana, Prometheus, CloudWatch, Datadog) for SLO and performance tracking.

Experience working in regulated or high-governance environments (e.g., HIPAA, SOC2, audit trails).

📌 Remote Product Manager - Lighting/Electrical (México)
🏢 ITJ
📍 México