AI Engineer + Knowledge Graph/Ontology
syrencloud· Data Science
About this role
AI Architect Engineer
Full-time [Location : Hyderabad/Remote] Reports to - Head Solutions
Syren Cloud is building a governed, no-code/low-code enterprise AI platform, and a flagship product powered by it. We're looking for an AI Engineer to own and architect the intelligence layer: the prompts and guardrails that shape agent behavior, and the canonical data model and knowledge graph those agents — and our separate Data Science team's models — reason over.
This role sits closer to architecture than a typical AI engineering position — you're not just implementing against a schema someone hands you, you're deciding what that schema should be.
What you'll do
Architecture & knowledge modeling:
- Own architectural decisions for how intelligence — prompts, retrieval, and agent logic — plugs into our platform's core: where inference happens, how data flows through our workflow orchestration layer, and when a capability belongs in the ontology layer versus a prompt versus the Data Science team's remit.
- Define and evolve the canonical data model — the shared representation of core business entities that every agent, workflow, and downstream model has to agree on, sitting on top of our platform's versioning and governance framework.
- Design automated ontology creation — turning raw source schemas from enterprise systems into structured entities, relationships, and synonyms programmatically, so a new data source onboards without hand-curated mapping every time.
- Build and maintain the knowledge graph connecting the core entities in our domain — the structure our product's retrieval and agent logic reasons over, and the foundation the Data Science team builds its models on top of.
Agent intelligence:
- Design and iterate on agent personas, system prompts, and guardrail logic — role/policy/content-safety checks, autonomy thresholds, and escalation rules.
- Own the evaluation harness: golden tests and prompt-regression checks that catch quality drift before an agent version ships.
- Validate agent behavior across test and production model configurations, working with our internal model registry.
- Tune retrieval quality for knowledge-grounded agents — chunking strategy, relevance testing, and search infrastructure tuning.
How you'll work:
- Build against written specs (spec → plan → tasks) rather than open-ended tickets, and write the specs yourself for the architecture and agent-intelligence surface you own.
- Use AI coding assistants as your primary implementation tool for scaffolding and iteration — your judgment goes into what to build and whether the output is actually correct, not into typing every line by hand.
- Review your own AI-assisted output as rigorously as you'd review a teammate's PR; verification is part of the job, not a step you skip because the code compiled.
- Partner closely with the Data Science team as a consumer of your canonical data model and knowledge graph — you own the structure, they own what gets modeled on top of it.
What we're looking for:
- Experience making architectural calls on a data or AI platform — not just implementing someone else's design.
- Hands-on experience with ontology design, knowledge graphs, or semantic/canonical data modeling — you've built one of these, not just read about them.
- Hands-on experience with LLM-based systems: prompt design, retrieval-augmented generation, and evaluating generative output quality.
- Strong Python fundamentals and comfort working against a modern backend service and its data model.
- Familiarity with agentic or workflow-orchestration patterns — multi-step graphs, tool-calling, trigger → retrieve → act → respond pipelines.
- Comfortable pair-programming with AI coding assistants as a primary tool, balanced with strong code-review instincts.
- Clear technical writing — specs are this role's primary interface with the rest of the team.
Nice to have:
- CPG / Retail / Supply chain domain knowledge
Frequently Asked Questions
Is the salary disclosed for the AI Engineer + Knowledge Graph/Ontology position at syrencloud?
The salary for this AI Engineer + Knowledge Graph/Ontology role at syrencloud is not publicly listed. Click "Apply Now" to learn more about the compensation package on their official careers page.
Is the AI Engineer + Knowledge Graph/Ontology role at syrencloud full-time or part-time?
This is listed as a FULL TIME position. It is posted as a AI Engineer + Knowledge Graph/Ontology role in the Data Science department at syrencloud.
Which team or department does the AI Engineer + Knowledge Graph/Ontology at syrencloud belong to?
This AI Engineer + Knowledge Graph/Ontology position is part of the Data Science department at syrencloud. See the full job description for more information about the team structure and responsibilities.
How do I apply for the AI Engineer + Knowledge Graph/Ontology position at syrencloud?
Click the "Apply Now" button on this page. You will be redirected to syrencloud's official application portal hosted on keka where you can submit your application directly.
When was the AI Engineer + Knowledge Graph/Ontology job at syrencloud posted?
This AI Engineer + Knowledge Graph/Ontology position at syrencloud was posted on Sep 8, 2026. Apply as soon as possible — early applications are often reviewed first.
AI Engineer + Knowledge Graph/Ontology
syrencloud
You'll be redirected to syrencloud's official application page on keka.