Technical Product Manager - AI Products

Location: Pune - On-Site

Type: Full-Time

Reporting To: Head of Engineering

ABOUT omniXM

omniXM is a fast-growing SaaS company building intelligent customer experience management products. Our flagship AI product, omniXMOS, includes a conversational survey analysis engine, omniSense, that processes real-world business feedback at scale. The system combines deterministic data tools, LLM-powered reasoning, multi-modal report generation, and document-grounded RAG - all served through a FastAPI backend and deployed on Azure.

We are scaling our Product team to drive the next phase of omniSense: smarter agentic workflows, richer insights, and AI experiences our customers can trust. We are looking for an experienced Technical Product Manager to own product development end-to-end and partner closely with our AI Engineering team.

THE ROLE

As our Technical Product Manager, you will own all aspects of the product life cycle - the technical, functional, and business foundations of our AI products. You will act as the liaison between cross-functional teams including AI Engineering, Design, QA, Change Management, Customer Success, and Sales. Your goal is to translate ambiguous business problems and emerging AI capabilities into clear, well-scoped, shippable products.

This is a senior, hands-on role. You will gather requirements, create wireframes and workflows, describe them clearly to the development team, work with QA to validate delivery, and personally validate that what ships matches the requirements. You will be the Product Owner for an AI engineering team building probabilistic, LLM-powered systems - which means you must be comfortable defining requirements and acceptance criteria for features that do not behave deterministically.

Please do not apply if you do not have 8+ years of experience creating BRDs / PRDs / FRDs, building wireframes and user flows, running scrum ceremonies, and leading the SDLC for software products.

ROLES & RESPONSIBILITIES

1. Product Vision & Roadmap

· Define and maintain the product roadmap for omniSense and adjacent AI capabilities, in alignment with business objectives and customer needs.

· Translate market signals, customer feedback, and AI engineering possibilities into a prioritized, outcome-driven roadmap.

· Make explicit build / buy / defer decisions and clearly communicate the rationale and trade-offs behind them.

2. Requirements & Specification

· Gather and elicit requirements from stakeholders, then create and maintain comprehensive BRDs / PRDs / FRDs detailing functional and non-functional requirements.

· Write clear acceptance criteria and definitions of done - including for AI/LLM features where outputs are probabilistic (quality bars, confidence thresholds, guardrails, and acceptable failure modes).

· Develop detailed wireframes, prototypes, and workflow / user-flow diagrams to communicate design and interaction concepts, including how users review, trust, and act on AI-generated output.

3. Sprint Planning, Delivery & Product Ownership

· Act as the Product Owner for the AI Engineering team: own and prioritize the backlog and ensure alignment with the product vision.

· Describe requirements, wireframes, and workflows to the development team via developer-ready stories and clear handoffs, removing ambiguity before and during a sprint.

· Lead and facilitate SCRUM ceremonies - sprint planning, daily stand-ups, reviews, and retrospectives - and own the sprint goal and the team's commitment.

· Drive sprint execution: track progress, remove blockers, and manage scope during the sprint to keep delivery on track.

· Monitor and improve team velocity, throughput, and predictability (burndown, capacity planning) to forecast delivery and continuously improve the team's cadence.

· Manage the product release process end-to-end, coordinating across engineering, design, QA, and business teams.

4. Validation & Quality

· Write thorough test cases and partner with QA to validate that delivered functionality meets the requirements - including evaluation of probabilistic AI output quality.

· Personally validate and sign off that what is built matches the intent and acceptance criteria of the requirements.

· Proactively identify and address product, technical, and AI-specific risks (hallucination, data quality, cost scaling).

5. Partnering with AI Engineering & Stakeholders

· Partner closely with AI engineers throughout the lifecycle, developing enough fluency in agents, RAG, prompting, fine-tuning, and model trade-offs (cost, latency, accuracy) to scope AI work credibly and de-risk uncertain capabilities.

· Define metrics and the evaluation strategy for AI features (e.g., answer relevance, faithfulness, deflection, cost-per-query) in collaboration with engineering.

· Serve as the primary point of contact for product queries, and communicate complex technical and AI concepts - including capabilities, limitations, and risks - clearly to non-technical stakeholders, customers, and sales leaders.

· Contribute to the continuous improvement of our product development processes.

· Own outcomes end-to-end with a strong bias for action - do what it ethically takes to get things across the line, including stepping in after hours when a launch or critical issue demands it.

QUALIFICATIONS

Required

· 8+ years of experience in technical product management, technical business analysis, or a similar role within a SaaS company.

· Proven ability to gather requirements and translate them into detailed technical specifications.

· Strong, demonstrated experience creating BRDs, PRDs, FRDs, wireframes, prototypes, and user flows.

· Excellent understanding of software development methodologies, particularly Agile/SCRUM, and experience acting as Product Owner.

· Experience writing comprehensive test cases and working with QA on acceptance/validation.

· Experience managing product releases and coordinating cross-functional teams.

· Working fluency with modern AI/LLM concepts - agents, RAG, prompting, fine-tuning, and the practical trade-offs of building with probabilistic systems - sufficient to partner credibly with AI engineers.

· Comfort using AI tools to accelerate the creation of artifacts (specs, wireframes, prototypes, diagrams, test cases).

· Exceptional communication, collaboration, and interpersonal skills; strong analytical and problem-solving abilities; ability to thrive in a fast-paced environment.

· Strong sense of ownership and a bias for action - a genuine drive to take things to completion and a willingness to step in when it matters, including outside normal hours when the situation demands.

Preferred

· Hands-on experience shipping AI / ML / LLM-powered features in production.

· Familiarity with AI evaluation concepts (offline eval, golden sets, LLM-as-judge, precision/recall, faithfulness).

· Experience defining product metrics and instrumentation, and running A/B experiments.

· Background in survey analytics, CSAT, or CX domains - a strong plus given our product focus.

· Familiarity with Azure or other major cloud platforms.

Education

· Bachelor's degree in Computer Science, Engineering, or a related technical field - or equivalent demonstrated experience.