Senior Technical PM, Applied AI

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 into future roadmap priorities. Requirements Professional Experience: Approximately 4–8 years of relevant experience in technical product management, applied AI engineering, research engineering, or a similar discipline, with demonstrated product ownership. Product Management Expertise: Proven ability to own complex technical products, deliver features to enterprise customers, and demonstrate measurable business outcomes. Strong product judgment and prioritization skills are essential. AI and LLM Knowledge: Deep understanding of large language models, reasoning capabilities, tool calling, multi-step task execution, agent orchestration patterns, evaluation methodologies, and production observability. Hands-On Technical Skills: Experience building AI agent prototypes, integrating APIs and MCP tools, designing evaluations, and debugging production failures. Comfortable experimenting directly with AI technologies rather than relying exclusively on specifications and documentation. Technical Fluency: Working knowledge of APIs, cloud environments, AI deployment workflows, and the technical considerations involved in delivering reliable enterprise AI products. Customer-Centric Approach: Ability to understand complex enterprise requirements and translate them into actionable product specifications, practical solutions, and measurable success criteria. Communication and Leadership: Excellent written and verbal communication skills, with the ability to align engineering, research, and commercial teams and present product strategy and outcomes clearly to executive stakeholders. Analytical and Problem-Solving Skills: Ability to interpret evaluation results, investigate system behavior, weigh technical trade-offs, and make evidence-based decisions about model quality, performance, reliability, and cost. Preferred Experience: Previous experience launching AI agent products within conversational AI or enterprise AI platforms, working directly with AI labs or model providers, or participating in early-access model evaluation programs. Additional Advantages: Familiarity with agent frameworks and protocols such as LangGraph and MCP, voice AI technologies, and regulated or mission-critical industries such as aviation, banking, healthcare, or payments. Experience collaborating with large systems integrators is also valuable. Demonstrated Technical Work: Publicly shareable projects, repositories, prototypes, blog posts, or presentations showcasing practical expertise in applied AI are an advantage. Benefits Remote Work: Remote position based in India, offering flexibility in how and where you work. Innovative Technology: Opportunities to work with advanced AI models, agent orchestration technologies, and emerging enterprise AI capabilities. Meaningful Impact: Direct influence on product strategy and the development of AI solutions used by large enterprise customers. Technical Ownership: Hands-on opportunities to experiment, prototype, evaluate, and improve production AI systems. Cross-Functional Collaboration: Work alongside engineering, research, data science, and business teams in a technically driven environment. Professional Growth: Exposure to emerging AI technologies, model providers, and complex enterprise deployment challenges. Compensation and Additional Benefits: Salary, healthcare coverage, bonuses, and other employment benefits were not specified in the original job description and should be confirmed during the recruitment process. Inclusive Workplace: Equal employment opportunity and a commitment to evaluating candidates based on their qualifications and relevant capabilities.