Senior Technical PM, Applied AI

Job description

**This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Technical PM, Applied AI based in India.** Join a fast-growing enterprise AI environment focused on transforming customer experiences through intelligent, autonomous AI agents. As a Senior Technical Product Manager, you will own the strategy, roadmap, and delivery of advanced AI agent capabilities, translating complex technical innovations into measurable business outcomes. You will work closely with engineering, AI research, data science, and enterprise customers to identify high-value opportunities and bring impactful solutions to market. This is a hands-on role where you will experiment with emerging models, build prototypes, design evaluations, and troubleshoot real-world agent behavior. You will shape how AI agents reason, use tools, manage memory, and collaborate with human teams while ensuring reliability, safety, and scalability. Your work will directly influence product adoption, operational efficiency, customer satisfaction, and business growth across global enterprise deployments. ### Accountabilities - Product Strategy and Roadmap: Own the multi-quarter product roadmap for AI agents, from discovery and prioritization to delivery, adoption, and outcome measurement. Make informed trade-offs and align product investments with customer needs and business objectives. - Business Impact and Performance: Define and track measurable outcomes, including autonomous resolution rates, cost per resolution, customer satisfaction, revenue expansion, and competitive performance. Use data to guide prioritization and demonstrate product value. - Customer and Stakeholder Collaboration: Work closely with enterprise customers, Customer Success, Sales, Delivery, and technical teams to identify high-impact problems and translate them into clear product requirements, user stories, success metrics, and dependency maps. - Cross-Functional Leadership: Coordinate engineering, AI research, data science, quality assurance, and go-to-market teams to establish priorities, manage dependencies, and ensure launch readiness. Lead alignment across teams without relying on direct reporting authority. - Planning and Execution: Establish and maintain product operating rhythms, including quarterly roadmap planning, monthly scope commitments, weekly execution reviews, and launch readiness assessments. - Product Launch and Adoption: Develop launch plans, instrumentation requirements, enablement materials, and success criteria. Monitor feature adoption across deployments and communicate product outcomes through regular performance reporting. - AI Agent Capabilities: Lead product development across agent reasoning and orchestration, multi-step task execution, tool and API integrations, memory and context management, human-in-the-loop workflows, and enterprise governance. - Hands-On AI Experimentation: Evaluate emerging models and capabilities from AI research labs and model providers. Build prototypes, develop agents, connect systems through APIs and Model Context Protocol (MCP), and provide evidence-based feedback to improve product decisions. - Evaluation and Quality Assurance: Define agent readiness scorecards and go/no-go criteria. Develop evaluation methods for task completion, tool reliability, policy compliance, latency, and cost, ensuring regression testing covers model, prompt, and tool changes. - Production Monitoring and Debugging: Analyze agent traces and investigate failures such as incorrect actions, missed escalations, tool-call errors, and memory gaps. Coordinate root-cause analysis and ensure significant issues result in corrective actions and regression tests. - Continuous Improvement: Guide model selection and routing decisions using real-world quality, latency, and cost data. Conduct post-release reviews and incorporate operational insights