Senior Technical PM, Applied AI
New
J
JobgetherEnterprise AI
Remote position based in IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- Approximately 4–8 years of relevant experience
- Required Skills
- RESTful APIsLLM
Requirements
- Approximately 4–8 years of relevant experience in technical product management, applied AI engineering, research engineering, or a similar discipline.
- Demonstrated product ownership and ability to manage complex technical products, deliver features to enterprise customers, and demonstrate measurable business outcomes.
- Strong product judgment and prioritization skills.
- Deep understanding of large language models, reasoning capabilities, tool calling, multi-step task execution, agent orchestration, evaluation methodologies, and production observability.
- Experience building AI agent prototypes, integrating APIs and MCP tools, designing evaluations, and debugging production failures.
- Working knowledge of APIs, cloud environments, AI deployment workflows, and technical considerations for reliable enterprise AI products.
- Ability to translate complex enterprise requirements into actionable product specifications, practical solutions, and measurable success criteria.
- 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 launching AI agent products in conversational AI or enterprise AI platforms, working with AI labs or model providers, or participating in early-access model evaluation programs.
- Advantageous: familiarity with LangGraph, MCP, voice AI technologies, regulated or mission-critical industries, or collaboration with large systems integrators.
- Advantageous: publicly shareable projects, repositories, prototypes, blog posts, or presentations demonstrating practical applied AI expertise.
Responsibilities
- Own the multi-quarter product roadmap for AI agents, from discovery and prioritization through delivery, adoption, and outcome measurement.
- Define and track outcomes including autonomous resolution rates, cost per resolution, customer satisfaction, revenue expansion, and competitive performance.
- Work with enterprise customers and internal teams to translate high-impact problems into product requirements, user stories, success metrics, and dependency maps.
- Coordinate engineering, AI research, data science, quality assurance, and go-to-market teams on priorities, dependencies, and launch readiness.
- Establish product operating rhythms for roadmap planning, scope commitments, execution reviews, and launch readiness assessments.
- Develop launch plans, instrumentation requirements, enablement materials, and success criteria, and report on feature adoption and product outcomes.
- Lead development of agent reasoning and orchestration, multi-step task execution, tool and API integrations, memory management, human-in-the-loop workflows, and enterprise governance.
- Evaluate emerging models, build prototypes and agents, connect systems through APIs and MCP, and provide evidence-based feedback for product decisions.
- Define agent readiness scorecards and evaluation methods, and ensure regression testing covers model, prompt, and tool changes.
- Analyze agent traces, investigate production failures, coordinate root-cause analysis, and incorporate operational insights into product improvements.
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