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).

📌 Product manager (México)
🏢 ITJ
📍 México