The Role

Job Title : AI Engineer

Location: Gurgaon, Haryana (On-site)

Experience: 1-3 years

What youll build

- LLM and agentic systems that reason over operational data - DCS/historian tags, time-series, sensor streams, and ISA-95-structured objects - not only PDFs.
- Retrieval and prediction over messy industrial reality: engineering documents, PIDs, incident reports, schematics, plus tag-level plant data.
- AI features where a wrong answer is a safety and liability event , not a UX bug. Correctness, abstention, and human-in-the-loop are the job.

Responsibilities

- Design, build, and iterate LLM-powered workflows - retrieval, routing, tool use, function calling, multi-step agents.
- Implement agentic apps that plan, call tools/APIs, and maintain state across tasks.
- Build RAG pipelines end-to-end: ingestion, chunking, embeddings, indexing, and latency-optimized retrieval - including layout-aware parsing of drawings and reports.
- Own prompt engineering and evaluation: golden datasets built with domain experts , A/B tests, guardrails, and metrics across latency, cost, quality, and safety.
- Productionize with observability (traces, tokens, failures), cost controls, and fallbacks (LangSmith / Langfuse / Arize or equivalent).
- Ship backend services and APIs (Python / FastAPI) integrating with data stores, vector DBs, and time-series sources.
- Handle deployment reality for industrial clients: VPC, on-prem, or air-gapped environments and self-hosted open models where data cant leave the plant.
- Collaborate with PM/Design and SMEs to translate requirements into reliable, safe, user-facing features.

Must have

- Hands-on with LLMs (OpenAI, Claude, Llama,



etc.) and orchestration frameworks (LangChain, LlamaIndex, or custom).
- Strong Python; RESTful services; clean, tested code.
- RAG in production - vector databases (Pinecone, Weaviate, FAISS, Qdrant) and embeddings.
- Solid grasp of agent patterns: tool calling, planning/execution, memory, workflow engines.
- Prompt design, safety/guardrails, and evaluation frameworks - with a real point of view on hallucination control and confidence/abstention.
- Cloud and deployment basics (AWS/GCP/Azure), Docker, Git, CI/CD.
- Strong debugging mindset and a bias to ship.

Solid plus

- MCP - we lean on it heavily; real familiarity is a signal were actively looking for.
- Self-hosted / on-prem inference: vLLM, quantized models, private deployment.
- Time-series, anomaly detection, or classical ML - the predictive side of industrial AI, not just LLM apps.
- Document AI and vision: OCR, layout parsing, multimodal models for drawings and reports.
- Model adaptation for cost/quality: fine-tuning, LoRA, distillation, small-model routing.
- Exposure to manufacturing, process safety, or industrial data.

Why this role

Youll build AI for a domain almost nobody has cracked - Indian process industries, at the frontier, with real clients and real plant data. The problems are hard because theyre safety-critical and the data is raw. Thats the point.

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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.

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