AI Engineer & Knowledge Graph/Ontology

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. Were 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 teams models - reason over.

This role sits closer to architecture than a typical AI engineering position - youre not just implementing against a schema someone hands you, youre deciding what that schema should be.

What youll do

Architecture & knowledge modeling:

• Own architectural decisions for how intelligence - prompts, retrieval, and agent logic - plugs into our platforms 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 teams 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 platforms 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 products 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 youll 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 youd review a teammates 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 were looking for:

• Experience making architectural calls on a data or AI platform - not just implementing someone elses design.
• Hands-on experience with ontology design, knowledge graphs, or semantic/canonical data modeling - youve 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 roles primary interface with the rest of the team.

Nice to have:

• CPG / Retail / Supply chain domain knowledge

Required Skills

RAG gen AI python AI/ML

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.