Product Manager

ITJ; Mexico; On-site; PHP 6,897,000 - 9,404,000; Full time

Job summary

ITJ is seeking a Technical Product Manager for the AI Platform to lead strategic planning, technical coordination, and execution across an enterprise AI/ML platform ecosystem. You will translate AI strategy into dependency-aware roadmaps, coordinate MCP integrations, and ensure secure, observable, cost-effective platform products.

The role requires a technical mindset with software development and microservices experience, strong collaboration with security, compliance, and business

Qualifications

• Bachelor’s degree in CS/CE/SE or related technical field.
• 5+ years asTechnical Product Manager/Owner/Lead for platform, infra, or developer tools.
• Hands-on software/AI/ML experience preferred.

Responsibilities

• Translate AI vision into multi-quarter platform roadmap with dependencies.
• Write Epics, User Stories, and Technical Specifications with DoR/DoD.
• Coordinate with architects and engineering squads on MCP, APIs, and microservices.
• Oversee E2E test planning and acceptance criteria for AI pipelines and integrations.
• Map and resolve cross-team technical dependencies across squads and data platforms.
• Own release planning, environment readiness, and rollout/rollback strategies.
• Collaborate on security, governance, and compliance within the platform.
• Track reliability, spend, and usage; drive FinOps for model inference.

Skills

Platform/API management

AI/LLM foundations

Software engineering

Education

Bachelor’s degree in Computer Science/Engineering

Tools

Jira

Confluence

MCP integrations

Job description

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:

• 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.
• 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.
• Advanced proficiency with Jira, Confluence, and Agile/Scrum frameworks.

Skills:

Hard Skills

• Technical Product Management (Platform/APIs)
• AI / LLM / Agentic Systems & ML Foundations
• Observability & SLI/SLO Monitoring

Soft Skills

• Technical Systems Thinking & Architectural Empathy
• Strategic Prioritization & Trade-off Analysis
• Cross-Functional Leadership & Stakeholder 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).