Senior Product Manager

KrisAtWork; Bengaluru; On-site; INR 1,800,000 - 4,200,000; Full time

Benefits offered by this job

Job summary

Kris@Work in Bengaluru, India, is seeking an AI-native Product Manager to own the end-to-end lifecycle of AI-driven capabilities, from predictive models to GenAI experiences, translating complex data and ML systems into scalable, customer-impacting products.

You will collaborate with ML engineers, data scientists, and business stakeholders to ship reliable, explainable AI products, establish metrics, run experiments, and drive measurable business value across revenue teams.

Qualifications

• 4+ years of Product Management experience with ownership of data and AI-powered products.
• Proven track record shipping predictive models and/or GenAI products to production.
• Strong understanding of supervised and unsupervised ML, feature engineering, data pipelines and experimentation.

Responsibilities

• Define and own the AI product vision and roadmap.
• Identify high-leverage prediction and generation opportunities across user workflows.
• Translate ambiguous business problems into clear AI-powered product hypotheses.
• Prioritize features using data, expected lift, confidence intervals and ROI modeling.
• Lead products powered by classification, regression, ranking, forecasting and anomaly detection.
• Ensure responsible AI practices and governance.

Skills

AI product management

Data science background

GenAI / LLMs knowledge

Stakeholder collaboration

Experimentation & measurement

Education

Bachelor's degree in technical/engineering/AI or related

Tools

OpenAI/ Anthropic

RAG

Vector databases

Prompt engineering

Job description

Kris@Work is the new generation AI-native Go-To-Market platform. It runs a B2B company's entire revenue engine, from finding the right accounts to closing and growing them. Revenue teams today juggle 25+ disconnected tools. Kris@Work replaces them with a single system, powered by its proprietary Kris Tell Intelligence™ layer and a multi-agent architecture that thinks, decides, and acts across the revenue cycle.

About the Role

We are looking for a AI-native Product Manager who blends product thinking, data science rigor, and comes with hands-on AI fluency. This role owns the end-to-end product lifecycle for AI-driven capabilities, ranging from predictive models to generative experiences, translating complex data & ML systems into scalable, customer-impacting products.

The ideal candidate has prior experience in data science or ML, deeply understands model behavior, trade-offs, and evaluation, and can partner seamlessly with engineering, ML, and business stakeholders to ship intelligent, impactful, reliable & explainable AI products.

What You’ll Do

• Define & own the AI product vision and roadmap, aligned with business outcomes and measurable customer value
• Identify high-leverage prediction and generation opportunities across user workflows
• Translate ambiguous business problems into clear AI-powered product hypotheses
• Prioritize features using data, expected lift, confidence intervals & ROI modeling

Predictive AI Ownership

• Lead products powered by classification, regression, ranking, forecasting & anomaly detection models
• Define prediction targets, labels, features, and success criteria in collaboration with AI/ ML engineering teams
• Drive decisions around model refresh cadence, retraining triggers & drift detection

Generative AI & LLM-Driven Experiences

• Own GenAI use-cases including summarization, recommendations, copilots, content generation & reasoning workflows
• Design prompt strategies, retrieval-augmented generation (RAG), tool-calling & memory architectures
• Define evaluation frameworks for GenAI quality (faithfulness, relevance, completeness, toxicity, hallucinations)

Data-Driven Product Execution

• Write clear, technical PRDs that include data inputs & outputs, model assumptions & constraints, evaluation metrics & guardrails, failure modes & fallback strategies
• Partner deeply with ML engineers to review feature pipelines, model choices & offline vs online evaluation results
• Make informed trade-offs between rules, heuristics, classical ML & LLMs

Experimentation & Measurement

• Design and own A/B tests, holdouts & phased rollouts for AI features
• Measure incremental impact, not just accuracy (conversion lift, time saved, win-rate, retention)
• Build feedback loops to improve models using human-in-the-loop signals
• Define north-star & secondary metrics for AI success

Cross-Functional Leadership

• Act as the product glue between ML, engineering, design, sales, customer success & leadership
• Translate technical AI concepts into clear business narratives for executives
• Partner with go-to-market teams on AI positioning, trust messaging & explainability
• Influence without authority, and set direction through clarity & data

AI Ethics, Trust & Reliability

• Ensure responsible AI practices around bias detection & mitigation, explainability & transparency as well as privacy, data governance & compliance
• Define guardrails, confidence indicators, and override mechanisms for AI outputs
• Proactively identify edge cases and failure scenarios before customers do

Who You Are & What Makes You Qualified

• 4+ years of Product Management experience, with significant ownership of data and/or AI-powered products
• Proven track record shipping predictive models and/or GenAI products to production
• Strong understanding of supervised & unsupervised ML, feature engineering & data pipelines, model evaluation & experimentation
• Practical experience with LLMs (OpenAI/ Anthropic), RAG, embeddings, vector databases, prompt engineering & evaluation
• Ability to reason about model performance, trade-offs & limitations
• Deep intuition for user workflows & decision-making under uncertainty
• Ability to connect model output to user action & business outcome
• Strong prioritization skills grounded in data, not intuition alone
• Experience building 0→1 AI products or scaling AI features across large user bases
• Experience with B2B SaaS, sales tech, CRM, or revenue intelligence platforms is preferred
• Prior hands-on experience as a Data Scientist, ML Engineer, or Applied Scientist is preferred
• Exposure to real-time systems, streaming data, or low-latency inference
• Experience working with enterprise customers and regulated data
• Familiarity with AI infra tools (feature stores, model registries, monitoring)
• Bachelor's degree in a technical, engineering, AI or related discipline; advanced degree is preferred