AI/ML + Graph Engineer

Hiring for: A US based well funded startup building a temporal agentic operating system for long-running, stateful enterprise AI workflows at large global enterprises.

Role: AI/ML + Graph Engineer

Positions: 1

Experience: 5 to 8 years

Location(s): Bengaluru

Type: On-site / Permanent

Salary: To be shared with shortlisted candidates

Notice Period: 30 days

Role:

This role will work closely on the core Temporal Agentic Operating System (TAOS) intelligence engine, focused on graph-driven AI systems rather than conventional GenAI wrappers or standard RAG applications. You will help design foundational intelligence layers that allow AI systems to reason over relationships, workflows, entities, policies, and evolving enterprise context across long-running business processes.

The work sits at the intersection of:

• Knowledge graphs
• Ontologies
• Graph algorithms
• AI/ML systems
• Decisioning engines
• Agent orchestration
• Stateful workflow reasoning

This is a systems-oriented AI role focused on durable intelligence infrastructure, not prompt engineering.

What you’ll bring

• Strong grounding in graph theory and graph-based reasoning systems
• Hands-on experience building or working with:
• knowledge graphs
• ontologies
• entity relationship systems
• graph databases
• semantic layers
• Strong understanding of graph algorithms, traversal strategies, ranking, inference, and relationship modeling
• Experience applying AI/ML techniques beyond standard LLM integrations, including areas like:
• reinforcement learning
• policy learning
• probabilistic systems
• decision intelligence
• optimization systems
• Ability to fine-tune or adapt small language models for domain-specific reasoning and decision workflows
• Strong Python skills with production-oriented engineering discipline
• Ability to work closely with orchestration and backend systems handling:
• state
• retries
• workflow continuity
• async execution
• long-running processes
• Systems-level thinking — understanding how intelligence layers behave under production constraints, not just in notebooks
• Curiosity toward agentic systems, workflow engines, and human-in-loop AI architectures

Experience with technologies/concepts such as

• Neo4j or other graph databases
• Knowledge graph frameworks
• RDF / ontology systems
• Graph embeddings and graph traversal systems
• Reinforcement learning or policy-learning systems
• Small language model fine-tuning
• Hybrid symbolic + neural reasoning approaches

What this role is NOT

• A prompt engineering role
• A chatbot development role
• A standard RAG pipeline implementation role
• A “wrap OpenAI APIs and ship demos” position
• A pure GenAI application layer role
• A frontend AI copilot role
• A research-only ML scientist position disconnected from production systems
• A graph visualization/dashboarding role
• A data analyst or BI engineering role
• A role focused only on embeddings/vector DB pipelines without deeper reasoning systems