Job Requirements

About Phenom and the Agents team

Phenom builds the AI talent platform that large enterprises use to hire, onboard and grow their people. The Agents team builds the AI agents that do hiring work end to end: the Sourcing Agent, the Intake Agent, outreach and candidate-fraud detection, along with the platform products that power them. Our bet is goal-driven hiring. A hiring manager states an outcome, such as "20 qualified ICU nurses in Dallas this quarter", and agents work backwards through intake, sourcing and outreach to deliver it. These agents run inside Phenom and inside the tools recruiters and managers already use, connected through open standards like MCP and agent-to-agent protocols.

The work is high-stakes: agents act on candidate data and influence employment decisions for some of the largest employers in healthcare, retail and financial services.

About the role

You will own a portfolio of products end to end: what they do, how well they do it, and whether customers get the hiring outcomes they paid for. The role is one part platform product manager and one part forward-deployed builder. You will define how our agents are designed, evaluated and governed, and you will also sit with enterprise customers to map their hiring workflows, demo working agents, and see deployments through to steady state. You may also own the platform products these agents depend on, such as the hiring ontology, sourcing and data collection, document intelligence, and the hiring-data pipelines that feed reinforcement learning. A excellent agent is only as good as the data, structure and feedback loops beneath it.

We want someone who builds, not someone who only manages a roadmap. You prototype with coding agents before writing a spec, you write your own evals, and you can explain exactly why an agent failed on a real customer's data.

Location: Hyderabad, India, working closely with customers worldwide, sales and leadership

What you'll do

Own an agent

Own the vision, roadmap and outcomes for an agent (for example, sourcing, intake or fraud), from problem definition through launch, adoption and iteration. Define each agent's job in outcome terms: what goal it accepts, what it is allowed to do on its own, and when it hands back to a recruiter or hiring manager.

Own the metrics that prove agents work,



such as goal-fulfilment rate, qualified candidates delivered, time to first qualified slate and recruiter acceptance, and report them in weekly business reviews.

Own the platform products that power agents

Own one or more shared platform products that agents build on, for example: Hiring ontology: the structured model of skills, roles, credentials and requirements that agents reason over.

Sourcing and data collection: how candidate and job data is acquired, enriched, deduplicated and kept fresh, with consent and provenance tracked.

Document intelligence: parsing and understanding resumes, job descriptions, credentials and onboarding documents.

Hiring-data RL pipelines: turning recruiter decisions, candidate responses and hiring outcomes into governed training and reinforcement-learning signals that make agents better over time.

Treat each platform product as a product: define its internal and external customers, roadmap, quality metrics and service levels.

Decide what belongs in the shared platform and what belongs inside a single agent, so new agents reuse capabilities instead of rebuilding them.

Set the quality bar with evals

Build and maintain evaluation sets drawn from real customer workflows, and decide what "good enough to ship" means for each agent.

Run regression, LLM-as-judge and human-review loops with engineering and data science, and turn failures into prioritised fixes.

Balance accuracy, latency and cost as models and prompts change.

Work forward-deployed with strategic customers

Lead discovery with enterprise talent-acquisition teams: map their hiring workflows, systems and constraints, and find where agents create the most value.

Build and present working agent demos that win over sceptical buyers, partnering with sales and solutions engineering on the technical win.

See deployments through hypercare to steady state, and handle escalations directly.





Turn what you learn in each deployment into reusable configurations, playbooks and platform features, so the tenth customer is faster than the first.

Must-have requirements

4+ years in product management, and 8+ years combining PM with adjacent product-building roles (forward-deployed engineering, solutions architecture, product consulting, founder, customer success, sales, etc.). At least 1 of those years spent shipping LLM-powered products used in production. Shipped agents, not just features. You have taken at least one AI agent or multi-step LLM workflow to real enterprise users, and can explain what broke in production and how you fixed it.

Evals fluency. You have designed evaluation sets, defined pass/fail criteria, and used evals to decide whether to ship.

Real technical depth. You understand tool calling, retrieval, orchestration, context management and the cost/latency trade-offs between models. You read API docs, and engineers trust your judgment in design reviews.

Hands-on builder. You use coding agents (Claude Code, Cursor, Codex or similar) regularly and prototype to test ideas before writing a PRD.

Enterprise customer skills. You have led discovery, mapped complex workflows and run demos with demanding B2B buyers, and you are comfortable owning escalations.

Outcome metrics. You define success as a business outcome, instrument it, and use the data to prioritise.

Clear writing and communication. You write crisp PRDs and POVs, and you explain technical AI concepts equally well to engineers, recruiters and executives.

Good-to-have requirements

Talent-acquisition domain knowledge: ATS, recruiting CRM, sourcing, programmatic job ads, or high-volume and hourly hiring, especially in healthcare or retail. Data and ML platform experience: ontologies or knowledge graphs, document parsing, data acquisition and enrichment, or feedback and RL pipelines that improve models from real-world outcomes.

Engineering or ML background: a computer science degree or prior work as a software or ML engineer.

Startup speed: time at a hyper-growth AI-native company, or running a 0-to-1 product inside a larger organisation.

Global collaboration: working from India with US enterprise customers and leadership across time zones.

📌 Senior Product Manager, Agents (Hyderabad)
🏢 Phenom People
📍 Hyderabad