Job Description Product Lead n Job Description About the Role n We are building a Context Engine that enables Agentic AI systems and LLMs to reason with reliable, structured and domain-specific context. n 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. n We are looking for a Product Lead who can work at the intersection of Product, Ontology/KG, AI, Engineering and Customer POCs. n 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. n 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 n · Translate the Context Engine vision into explicit MVP capabilities, use cases and build priorities. n · Work closely with the CPO and founders to convert ideas and hypotheses into an actionable roadmap. n · Track MVP progress, dependencies, risks and decisions. n · Identify gaps, ambiguity and blockers and drive them toward resolution. n · Ensure that the MVP remains focused on its intended customer and business outcomes. n · Participate actively in product and technical discussions rather than simply tracking tasks. 2. Work Closely with Ontology & Knowledge Graph Teams n · Understand and contribute to the design of domain ontologies and Knowledge Graphs. n · Help identify entities, classes, properties and relationships required to represent domain knowledge. n · Review and refine semantic relationships and terminology. n · Contribute to manual triple definition and semantic modelling. n · Ensure that relationships accurately represent the intended meaning and domain behaviour. n · Maintain consistency in terminology and ontology definitions. n · Work with engineers to translate domain concepts into machine-readable representations. 3. Research & Technical Reasoning n · Research emerging approaches in Ontology, Knowledge Graphs, RDF / semantic technologies, LLM grounding, RAG, Agentic AI, Context Engineering and Knowledge Representation. n · Compare alternative technical approaches and communicate their implications to the team. n · Form hypotheses and help design experiments to validate them. n · Analyze test results and convert learnings into product or technical improvements. n · Challenge assumptions constructively when evidence suggests a different approach. 4. Facilitate Between Engineering Teams n · Act as the connective layer between the teams involved in building the Context Engine. n · Work with Ontology/KG engineers, backend/application engineers, AI/LLM engineers, LinkEye engineering/product teams,



testing/QA teams and production/DevOps teams. n · Facilitate understanding and decisions rather than simply passing requirements between teams. n · Ask and drive clarity around the problem, assumptions, evidence, customer need, technical implications and MVP priority. 5. Customer Engagement & POCs n · Participate in customer discovery and technical discussions. n · Understand customer problems and identify the context required to solve them. n · Support and drive Context Engine POCs. n · Define POC scenarios, test cases and expected outcomes. n · Work with users to understand failures, gaps and unexpected behaviour. n · Translate customer feedback into actionable insights for the build team. n · Identify patterns across POCs that can influence the product roadmap. 6. Product & Technical Documentation n · Own and maintain documentation required to build, explain and evolve the Context Engine. n · Product documentation: product vision, problem statement, use cases, MVP scope, capabilities, roadmap, POC scenarios, success criteria and limitations. n · Technical documentation: architecture, data/context flows, ontology/KG concepts, semantic relationships, integration points, technical decisions, experiments, test methodology and known limitations. n · Ontology documentation: entity/class definitions, property definitions, relationship definitions, semantic rules, examples and modelling guidelines. n · Explain technically complex concepts clearly to both engineers and non-technical stakeholders. 7. Product Articulation & Marketing Support n · Work with the CPO and marketing/content teams to articulate what the Context Engine is, why it is required for Agentic AI, what problem it solves, how it differs from conventional RAG/LLM approaches, what the MVP demonstrates and what customer use cases it addresses. n · Convert complex technical concepts into clear, accurate and compelling product narratives without oversimplifying the technology. 8. Production Handover n · Define MVP acceptance criteria. n · Ensure product and technical documentation is complete. n · Capture architectural and operational learnings. n · Document known limitations and future requirements. n · Work with the production team to prepare the handover. n · Support the transition from experimental MVP to production-ready product. What We Are Looking ForCore Requirements n · Conceptual understanding of Ontology and Knowledge Graphs. n · Ability to reason about semantics, entities, relationships and domain meaning. n · Ability to conduct technical research independently.



n · Ability to translate ambiguous ideas into structured product requirements and experiments. n · Ability to understand complex technical concepts and explain them clearly. n · Strong analytical and reasoning ability. n · Excellent written and spoken English. n · Strong technical writing capability. n · Comfortable working with engineers and technical teams. n · Strong collaboration and mediation skills. n · Comfortable interacting with customers/users during POCs. n · Strong ownership mindset. Highly Preferred n · Prior experience working with the network-management/domain context. n · Understanding of RDF, OWL, SPARQL or other semantic technologies. n · Aware of Neo4j or other Knowledge Graph platforms. n · Understanding of LLMs, RAG and Agentic AI. n · Experience working with AI/ML or developer-oriented products. The Kind of Person We Are Looking For n You may be a good fit if you enjoy moving between different worlds: n Domain knowledge ↔ Ontology ↔ Engineering ↔ Product ↔ Customer n You do not have to be the deepest expert in every technology. But you should be someone who can: n · Understand n · Question n · Research n · Reason n · Model n · Test n · Document n · Communicate n You should enjoy working in an environment where the product is still being discovered and where new questions are often more valuable than immediately having answers. n You should be comfortable saying: "I don’t know yet. Let me research it, test the assumption and come back with evidence." What Success Looks Like n · The Context Engine MVP has a clear and continuously evolving product definition. n · Ontology/KG concepts are semantically consistent and well documented. n · Engineering teams have clarity on what they are building and why. n · Technical decisions are supported by research and experimentation. n · MVP progress, risks and dependencies are visible. n · Customer POCs generate meaningful product learning. n · Product and technical documentation are maintained throughout the build. n · The CPO and founders have a reliable partner for day-to-day MVP execution. n · The validated MVP can be confidently handed over to the production team. This Role Is NOT n · A Jira/task tracking role n · A Scrum Master role n · A conventional project coordinator role n · A documentation-only role n · A pure ontology/KG engineering role n · A pure Product Manager role n It is a hands-on technical product and knowledge role that combines domain understanding, semantic modelling, research, product thinking, technical communication and cross-team collaboration. Suggested Experience n 1-2 years of relevant experience preferred. n Candidates with strong analytical ability, ontology/KG exposure and relevant Product domain experience may be considered even if they do not match every item above. Role Details n Reporting: CPO / Product Leadership n Employment Type: [Full time] n n Location: [Hybrid] n

📌 Product Engineer (Bengaluru)
🏢 LinkEye
📍 Bengaluru