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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Product Owner - **Company:** Laiba Technologies LLC - **Location:** Redmond, WA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Microsoft Azure, Computer Engineering, Data Integration, Python (Programming Language), Software Product Management, OpenAI, Microsoft Copilot, Usage Analysis, Design of Telemetry/logging Agents, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Agentic-AI, Microsoft Copilot Studio, AI Platforms, Low-code, Machine Learning Operations, Software Coding, Api Management - **Published:** October 20, 2026 - **Apply:** https://www.dice.com/job-detail/4ff9538e-e441-4366-99b1-d3c46eb40229 ## About the Role The ideal candidate is technically fluent in Generative AI, Agentic AI, LLMs, and RAG; comfortable building and testing prototypes themselves; and driven by business impact rather than process. You will act as the voice of the customer inside the squad and the voice of the squad with customer leadership., * Bachelor's degree in Computer Science & Engineering, Data Science, or a related field (or equivalent experience). * 7+ years in product management, product ownership, technical program management, solutions engineering, or consulting. * 2+ years hands-on delivering GenAI, LLM, or agentic AI solutions into production (not only roadmaps or strategy). * Practical experience with prompt engineering, RAG, vector databases, agent frameworks, and tool/API integration. * Ability to build working prototypes yourself (low-code agent builders, notebooks, scripting, or similar). * Experience designing AI evaluations and quality metrics for LLM-based systems. * Understanding of AI architecture, APIs, data integration, LLMOps, and production readiness. * Comfort working directly with customers in ambiguous, fast-moving environments. * Strong prioritization and the judgment to say no, or to stop work that is not delivering value. * Excellent communication and storytelling, including executive-level stakeholder management. * Bias for action and ownership of outcomes. Preferred Qualifications * Prior experience in a forward deployed, solutions engineering, or customer-embedded delivery model. * Experience building Copilots, multi-agent systems, or enterprise agentic workflows. * Familiarity with Azure AI Foundry, Azure OpenAI, Copilot Studio, Power Platform, and the Microsoft Copilot ecosystem. * Experience with AI governance, Responsible AI, and enterprise security and compliance reviews. * Knowledge of telemetry, product analytics, A/B testing, and adoption measurement. * Basic Python or similar coding skills ## Description Hands-on AI Product Owner to join our Forward Deployed Engineering (FDE) squad, a small, embedded team that works directly with customers to turn high-value business problems into production AI solutions in weeks, not quarters. This is not a traditional product management role. You will sit alongside customer stakeholders and engineers, discover where AI can create measurable value, shape the solution in real time, and own the outcome from first prototype through production adoption., * Embed with customer teams to understand workflows, pain points, and data, and identify the highest-value AI and agentic use cases. * Rapidly frame problems into testable hypotheses, and define what "good" looks like before anything is built. * Prototype hands-on using prompt engineering, low-code agent tools, and AI platforms to validate ideas with users within days. * Own the squad backlog in ADO, writing lean user stories and acceptance criteria tied to business outcomes, not just features. * Partner with the AI Architect and Forward Deployed Engineers on agent design, tool and API integrations, RAG strategy, and model selection. * Define and own evaluation sets (golden datasets, test cases, rubrics) and use eval-driven development to decide when a solution is ready to ship. * Make pragmatic trade-offs between quality, latency, cost, and risk, and explain them clearly to customer stakeholders. * Drive solutions from pilot to production, including security, compliance, and operational readiness. * Own adoption: user onboarding, feedback loops, telemetry, and iteration after go-live. * Measure and report business impact (time saved, cost reduced, quality improved) and build the case for scaling or stopping. * Apply Responsible AI principles, including guardrails, human-in-the-loop design, hallucination handling, and monitoring for drift and misuse. * Capture reusable patterns, components, and playbooks so each engagement makes the next one faster. * Build trusted relationships with customer leaders and identify follow-on opportunities.