Agentic AI Engineer

Role As our Agentic AI Engineer, you will build the intelligence layer that sits on top of our knowledge graph: the part of APAS that turns structured regulatory and operational data into deterministic, citation-backed answers and, over time, autonomous agentic workflows. This is a specialist role. We need someone who lives in large language models, retrieval architectures, and agent design, not a generalist ML engineer picking this up as one of several things they do. Every model you build depends on data structured below you, and every decision you make about grounding, hallucination control, and citation accuracy has to hold up to the same standard the rest of APAS is built on: deterministic, defensible, provable. This is not a job you'll do in a silo . Expect tight and transparent communication loops across our team. Must Have Minimum 3 years of hands-on experience building and shipping production ML/AI systems, focused specifically on LLMs, NLP, or generative AI Minimum 3 years of production Python experience, with frameworks like PyTorch, LangChain, or Hugging Face Transformers Deep, practical experience with retrieval-augmented generation (RAG) architectures, including chunking strategies, embeddings, vector search, and hybrid retrieval that combines structured and unstructured data Proven experience building agentic systems: multi-step reasoning, tool use, task planning, and orchestration (LangGraph, LlamaIndex, AutoGen, or a custom-built framework) Strong grounding in prompt engineering and techniques for hallucination control, citation accuracy, and deterministic output, especially in high-stakes or regulated domains Experience integrating LLM applications with knowledge graphs, ontologies, or structured data sources such as SPARQL/RDF. You don't need to own the graph, but you need to work fluently with it Experience working with function-calling and tool-use models — open-source (e.g., Hermes, and other fine-tuned Llama/Mistral variants built for agentic use) as well as proprietary (e.g., Claude, GPT) — and knowing when a self-hosted, fine-tuned model is the right call versus a foundation model API Experience fine-tuning or adapting open-source or foundation LLMs for domain-specific applications Experience designing evaluation frameworks for LLM output quality, accuracy, and safety, not just spot-checking outputs by hand Comfortable working with cloud infrastructure (AWS or Azure) and collaborating with DevOps on deployment, even though you won't own infrastructure yourself Strong communicator who can explain model behavior and trade-offs clearly to non-technical stakeholders, including our go-to-market and leadership team Nice to Have Experience in regulated, compliance-heavy, or government and public-sector domains, where deterministic and defensible AI output genuinely matters Familiarity with knowledge graph query languages (SPARQL, Cypher) beyond a surface level Experience building digital-twin or simulation-style AI features Prior startup experience, especially pre-revenue or early-stage, and comfort building from zero with a high degree of ambiguity Open-source contributions to LLM tooling, agent frameworks, or RAG libraries Exposure to the water utility, civil engineering, or environmental regulatory space Location India, Remote Compensation $1,500 - $2,500 per month