Product Engineer

Location: Bengaluru

Product Lead


Job Description
About the Role We are building a Context Engine that enables Agentic AI systems and LLMs to reason with reliable, structured and domain-specific context.
The Context Engine uses Ontology, Knowledge Graphs and semantic relationships to represent domain knowledge and provide AI systems with the right context at the right time.
We are looking for a Product Lead who can work at the intersection of Product, Ontology/KG, AI, Engineering and Customer POCs.
This is not a conventional project-management role. You will be expected to understand the technology at a conceptual and working level, reason with engineering teams, conduct research, contribute to ontology/KG development, test ideas, document the technology, interact with users and help shape the MVP roadmap.
You will work closely with the CPO, founders/ideators and engineering teams to take the Context Engine from concept → MVP → validated POCs → production handover.
What You Will Do1. Own Day-to-Day MVP Product Execution
· Translate the Context Engine vision into clear MVP capabilities, use cases and build priorities.

· Work closely with the CPO and founders to convert ideas and hypotheses into an actionable roadmap.

· Track MVP progress, dependencies, risks and decisions.

· Identify gaps, ambiguity and blockers and drive them toward resolution.

· Ensure that the MVP remains focused on its intended customer and business outcomes.

· Participate actively in product and technical discussions rather than simply tracking tasks.
2. Work Closely with Ontology & Knowledge Graph Teams
· Understand and contribute to the design of domain ontologies and Knowledge Graphs.

· Help identify entities, classes, properties and relationships required to represent domain knowledge.

· Review and refine semantic relationships and terminology.

· Contribute to manual triple definition and semantic modelling.

· Ensure that relationships accurately represent the intended meaning and domain behaviour.

· Maintain consistency in terminology and ontology definitions.

· Work with engineers to translate domain concepts into machine-readable representations.
3. Research & Technical Reasoning
· Research emerging approaches in Ontology, Knowledge Graphs, RDF / semantic technologies, LLM grounding, RAG, Agentic AI, Context Engineering and Knowledge Representation.

· Compare alternative technical approaches and communicate their implications to the team.

· Form hypotheses and help design experiments to validate them.

· Analyze test results and convert learnings into product or technical improvements.

· Challenge assumptions constructively when evidence suggests a different approach.
4. Facilitate Between Engineering Teams
· Act as the connective layer between the teams involved in building the Context Engine.

· Work with Ontology/KG engineers, backend/application engineers, AI/LLM engineers, Link Eye engineering/product teams, testing/QA teams and production/Dev Ops teams.

· Facilitate understanding and decisions rather than simply passing requirements between teams.

· Ask and drive clarity around the problem, assumptions, evidence, customer need, technical implications and MVP priority.
5. Customer Engagement & POCs
· Participate in customer discovery and technical discussions.

· Understand customer problems and identify the context required to solve them.

· Support and drive Context Engine POCs.

· Define POC scenarios, test cases and expected outcomes.

· Work with users to understand failures, gaps and unexpected behaviour.

· Translate customer feedback into actionable insights for the build team.

· Identify patterns across POCs that can influence the product roadmap.
6. Product & Technical Documentation
· Own and maintain documentation required to build, explain and evolve the Context Engine.

· Product documentation: product vision, problem statement, use cases, MVP scope, capabilities, roadmap, POC scenarios, success criteria and limitations.

· Technical documentation: architecture, data/context flows, ontology/KG concepts, semantic relationships, integration points, technical decisions, experiments, test methodology and known limitations.

· Ontology documentation: entity/class…