Senior AI Product Owner
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 2d ago
The date the source published, not the day we noticed it (2026-10-08). Last seen at its source 1h ago.
We have tracked this listing since 8 Oct 2026 (2 days). The employer's own board has carried it every time we have read it, most recently 1 hour ago.
Is it remote?
The listing says yes
The location field doesn't say remote, so our assessment is based on the title or the description. Read the listing before applying.
Who may apply?
LATAM
The description states no restriction of its own. This is the source's own tag.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Greenhouse employer's own board first seen 2d ago · last seen 1h ago
The listing
AI Product Owner
Location/Region
Latin America | 100% Remote
About CodeRoad
CodeRoad provides end-to-end software development services, helping businesses scale with ideal infrastructure solutions. From staff augmentation to dedicated IT teams and general software engineering, our nearshore technology services empower businesses to thrive in an ever-evolving digital landscape.
About the Role
As an AI Product Owner at CodeRoad, you will anchor the discovery, strategy, and execution of cutting-edge AI-powered solutions for our enterprise client. You will lead a high-performing cross-functional POD—comprising AI research, data engineering, front-end development, and DevOps talent—shaping raw business requirements into robust, production-grade LLM applications. Working at the intersection of AI orchestration, enterprise data access, and strict compliance environments, you will establish what "good" looks like by driving feature scope, quality evaluation benchmarks, and system performance standards.
This role is critical to successfully delivering secure, highly compliant, and scalable AI agents and RAG implementations. By bridging the gap between non-technical business leaders and technical engineering teams, you will turn complex AI capabilities into tangible business ROI while managing project roadmaps, risk mitigation strategies, and cloud consumption budgets.
Key Responsibilities
- Own the AI Product Backlog & Strategy: Identify, define, and prioritize AI use cases based on business value, technical feasibility, and data readiness, translating user needs into granular stories with rigorous acceptance criteria and edge-case handling.
- Establish AI Quality & Evaluation Frameworks: Collaborate with business experts to curate golden evaluation datasets, review agent outputs, monitor hallucination risks, and iterate on prompts, guardrails, and context retrieval systems to sign off on release readiness.
- Orchestrate Delivery Across Cross-Functional PODs: Anchor sprint planning, grooming, and release scoping with engineers and architects to balance rapid innovation with operational stability, cost control, and performance latency.
- Drive Stakeholder & Compliance Alignment: Serve as the primary contact for client leadership, running workshops, conducting feature demos, and securing necessary data-privacy and governance sign-offs in compliance-sensitive environments.
- Manage Project Operations & Risk Log: Proactively manage milestones, track cloud/LLM costs against budget, and maintain an updated risk register encompassing model drift, data latency, and infrastructure dependencies.
Requirements
- 4+ years of hands-on experience as a Product Owner or Product Manager delivering software products, with direct experience shipping LLM-powered applications (e.g., AI agents, RAG architectures, document extraction, conversational AI).
- Deep understanding of LLM evaluation methodologies, prompt engineering workflows, guardrail implementations, and observability/telemetry metrics (accuracy, latency, token consumption).
- Strong technical depth to evaluate engineering trade-offs around model selection, data access patterns, vector search, and infrastructure security with AI and data engineers.
- Demonstrated experience operating within Agile environments utilizing tools like Jira and Confluence to document technical user stories, decision logs, and API/data requirements.
- Proven track record in client-facing consulting or senior stakeholder management, displaying an ownership mindset and executive-level delivery skills.
- Advanced English fluency (C1+ spoken and written) for seamless collaboration with North American client partners.
Nice to Have
- Experience building AI solutions within Financial Services or heavily regulated sectors subject to data privacy and information barriers.
- Hands-on familiarity with Azure AI ecosystem and the broader Microsoft enterprise data stack.
- Operational experience working with client-side Managed Service Providers (MSPs) and third-party IT outsourcers.
- Exposure to modern AI orchestration frameworks (e.g., LangChain, LlamaIndex, AutoGen) and LLM observability tools (e.g., LangSmith, Phoenix, Arize).
What You’ll Love
- 100% Remote work flexibility
- Holidays off aligned with your country’s calendar
- Paid Time Off (PTO)
- Health insurance assistance
- Competitive USD compensation
- Growth opportunities in a fast-scaling tech environment