Roles & Responsibilities:

AI Platform & RAG
Build and maintain enterprise AI applications and assistants for engineering, sales, marketing, procurement, and support.
Build RAG systems on company knowledge bases.
Build document ingestion pipelines for PDFs, manuals, brochures, and specs using tools like Docling and LlamaParse.
Create and maintain vector databases (Qdrant), with embedding and reranking models (e.g. BGE-M3, BGE-Reranker).
Tune retrieval pipelines for accuracy and evaluate answer quality (e.g. RAGAS).

Agentic AI
Build autonomous, multi-step AI agents using LangGraph or similar.
Integrate agents with internal systems (ERP, CRM) via MCP servers.
LLM Integration
Integrate self-hosted (DeepSeek, Qwen via vLLM) and API LLMs (OpenAI, Claude, Gemini, OpenRouter).
Optimize prompts; apply safety controls (e.g. NeMo Guardrails).

Preferred candidate profile

We are building a self-hosted,



enterprise-grade AI platform for our manufacturing, engineering, sales, and marketing teams. We need a hands-on AI Engineer to help build and maintain it working across LLMs, RAG, autonomous agents, vector databases, document intelligence, and GPU-based model serving, using AI coding assistants like Claude Code and Codex to move rapid. This is a builder's role: you will ship production systems, not prototypes.

Workflow Automation
Build business automation in n8n.
Connect AI with ERP, CRM, email, WhatsApp, and internal apps.

Backend & Infrastructure
Build REST APIs with FastAPI.
Deploy with Docker and Docker Compose; work with GPU-based model serving (vLLM).
Monitor with observability tooling (Langfuse, Grafana, Prometheus).

📌 Artificial Intelligence Engineer Noida
🏢 Roto Pumps
📍 Noida