Product Manager (AI Platform)
Lexsi Labs · Mumbai Metropolitan Region
- On site
- Full-time
- $150,000 / year
- Mumbai Metropolitan Region
Job highlights
- Own AI/ML platform features end-to-end.
- Define product specs and acceptance criteria.
- Collaborate with AI researchers and engineers.
- Ensure feature correctness and effective testing.
- Drive product velocity and user value.
About the role
Product Manager AI Platform
Lexsi Labs is a leading frontier lab focused on building aligned, interpretable, and safe Superintelligence. Our mission is to build AI systems that are powerful, transparent, and trustworthy by design. We operate with a flat structure, fast feedback loops, and an expectation that everyone deeply understands what they ship.
About the Role
As a Product Manager at Lexsi.ai, you will own the end-to-end lifecycle of ML-driven platform features. This is not a coordination role. You will be responsible for problem definition, technical clarity, correctness, evaluation, and quality. You will work closely with AI Researchers, AI engineers, SDEs, and QA to ensure every feature behaves as intended, is testable effectively, and delivers real value to users. We are explicitly looking for someone with a strong AI/ML background who has grown into product ownership, not a traditional PM learning ML on the job.
Responsibilities
- Own product requirements and execution for AI/ML and platform-heavy features from problem definition to production rollout.
- Translate complex AI behavior into precise, testable product specifications and acceptance criteria.
- Work deeply with AI Researchers to collect the details of the components created and AI engineers and SDEs to ensure implementations match intended algorithms, assumptions, and constraints.
- Define what “correct” means for each feature, including metrics, evaluation logic, edge cases, and failure modes.
- Work with QA and testing teams to design meaningful test strategies that reflect real ML behavior, not just happy paths.
- Break down ambiguous product roadmaps or experimental ideas into shippable, scalable product increments.
- Prioritize features and technical debt based on user impact, system risk, and long-term platform scalability.
- Act as the quality bar-raiser by catching conceptual gaps early and preventing flawed features from shipping.
Requirements
- Prior experience as an ML engineer, data scientist, or research engineer.
- Experience building developer platforms, ML tooling, or infra-heavy products.
- Strong foundation in Deep Learning, LLMs, machine learning or data science with hands-on experience building or deploying AI systems.
- Familiarity with testing strategies for ML systems, including offline evaluation, simulation, and monitoring.
- Experience working closely with AI engineers on model behavior, evaluation, and performance trade-offs for multiple modalities - LLMs, Tabular, Text and Agentic systems.
- Ability to reason about ML correctness, limitations, and failure modes, not just UX or timelines.
- Experience owning product features end-to-end, either formally as a PM or informally as a technical lead.
- Comfortable writing precise specs, acceptance criteria, and evaluation plans that engineers and testers can execute against.
- Strong communication skills across research, engineering, QA, and leadership without dilution of technical meaning.
Nice to Have
- Exposure to interpretability, alignment, model observability, or AI safety work.
- Startup experience where product ambiguity and technical depth coexist.
What Success Looks Like
- Features ship with clear intent, correct behavior, and well-defined evaluation criteria.
- QA and testing teams understand what they are testing and why it matters.
- Engineering rework drops because specs capture real constraints upfront.
- Product velocity increases without sacrificing correctness or trust.
Key skills/competency
- Product Management
- AI/ML Platform
- Deep Learning
- LLMs
- Machine Learning
- Data Science
- Developer Platforms
- Product Requirements
- ML Engineering
- Testing Strategies
Skills & topics
- Product Manager
- AI Platform
- Machine Learning
- Deep Learning
- LLMs
- Data Science
- ML Engineering
- Developer Platforms
- AI Safety
- Product Development
How to get hired
- Tailor your resume: Highlight ML engineering, data science, or research experience. Emphasize AI/ML platform product ownership.
- Showcase AI/ML expertise: Detail your hands-on experience with Deep Learning, LLMs, or deploying AI systems.
- Demonstrate technical rigor: Provide examples of writing precise specs, evaluation plans, and managing ML testing strategies.
- Quantify achievements: Use data to show how your features improved product velocity or correctness.
- Research Lexsi Labs: Understand their focus on aligned, interpretable, and safe Superintelligence.
Technical preparation
Review AI/ML fundamentals and deployment.,Study ML testing and evaluation strategies.,Practice writing precise technical specifications.,Familiarize with developer platform concepts.
Behavioral questions
Describe a complex AI feature you launched.,How do you define 'correctness' for ML?,How do you handle ambiguity in product roadmaps?,How do you ensure clear communication with engineers?
Frequently asked questions
- What is the primary focus of Lexsi Labs as a company?
- Lexsi Labs is focused on building aligned, interpretable, and safe Superintelligence, with a mission to create AI systems that are powerful, transparent, and trustworthy by design.
- Is this a traditional Product Manager role at Lexsi Labs?
- No, this is not a traditional PM role. Lexsi Labs is seeking someone with a strong AI/ML background who has grown into product ownership, not a traditional PM learning ML on the job.
- What kind of background is required for the Product Manager (AI Platform) role?
- The ideal candidate has prior experience as an ML engineer, data scientist, or research engineer, with a strong foundation in Deep Learning, LLMs, or machine learning and hands-on experience deploying AI systems.
- What technical skills are essential for this Product Manager role?
- Essential technical skills include a strong foundation in AI/ML, experience building developer platforms or ML tooling, understanding of ML testing strategies, and the ability to reason about ML correctness and limitations.
- How does Lexsi Labs approach product development and team structure?
- Lexsi Labs operates with a flat structure, fast feedback loops, and an expectation that everyone deeply understands what they ship. The focus is on deep technical rigor and hands-on ownership.
- What does success look like for a Product Manager at Lexsi Labs?
- Success is measured by features shipping with clear intent and correct behavior, effective QA understanding, reduced engineering rework due to precise specs, and increased product velocity without sacrificing correctness or trust.
- Is experience with specific AI modalities required for this role?
- Experience working closely with AI engineers on model behavior and evaluation across modalities like LLMs, Tabular, Text, and Agentic systems is highly valued.
- What are the 'nice to have' qualifications for this position?
- Nice-to-have qualifications include exposure to interpretability, alignment, model observability, or AI safety work, as well as startup experience where product ambiguity and technical depth coexist.