Ai Ml Engineer

2 weeks ago

Pune, Maharashtra, India Inorbvict Healthcare Full time ₹ 20,00,000 - ₹ 25,00,000 per year

Key Responsibilities:

•      Architect, develop, and scale advanced Conversational AI Systems powered by LLMs, RAG, and Agentic AI workflows

•      Implement Retrieval-Augmented Generation (RAG) pipelines using tools like LangChain, LangGraph, LlamaIndex, and custom retrieval stacks

•      Engineer agentic behaviors with tools like AutoGen, CrewAI, LangGraph, or custom frameworks for multi-step reasoning and tool usage

•      Apply prompt engineering, fine-tuning, and LoRA/QLoRA techniques to customize LLMs (e.g., OpenAI GPT-4/5, Mistral, Claude, LLaMA)

•      Design pipelines to minimize hallucination and toxic output, ensuring factual accuracy, safety, and alignment

•      Integrate structured (e.g., MongoDB, PostgreSQL) and unstructured knowledge bases into the chatbot memory & retrieval mechanisms

•      Build and manage RAG-ready data pipelines (chunking, embedding, vector stores) using FAISS, Weaviate, Pinecone, Milvus, or Chroma

•      Optimize chatbot performance and latency using techniques like quantization, ONNX runtime, vLLM, or DeepSpeed

•      Develop secure, robust APIs and backend services using FastAPI, Flask, , or Go

•      Collaborate with AI researchers and product teams to turn prototypes into production-ready systems

•      Conduct continuous evaluation using BLEU, ROUGE, BERTScore, truthfulness metrics, and user feedback loops

Required Skills & Technologies

LLMs & Gen AI

•      OpenAI (GPT-4/5 APIs, Assistants API, Function Calling)

•      Hugging Face Transformers, Diffusers

•      Mistral, Claude, Mixtral, LLaMA, Falcon, Gemma, Phi-2

Agentic AI Frameworks

•      LangGraph, AutoGen, CrewAI, MetaGPT, BabyAGI

•      Custom agent orchestration with tools & memory

•      Event-driven or graph-based agent workflows

RAG & Retrieval Systems

•      LangChain, LlamaIndex, Haystack

•      Pinecone, Weaviate, FAISS, Chroma, Milvus

•      Document chunking, embedding, and indexing strategies

Data & Memory

•      MongoDB, PostgreSQL, Redis, Vector DBs

•      JSON-based memory stores, hybrid search systems

•      Session memory, short/long-term memory optimization

Fine-Tuning & Optimization

•      PEFT, LoRA, QLoRA, SFT, RLHF

•      vLLM, DeepSpeed, Hugging Face PEFT

•      Triton, ONNX, TensorRT (optional but a plus)

Tools & Engineering

•      Python (primary), , Go (bonus)

•      FastAPI, LangServe, Docker, Kubernetes

•      Git, CI/CD, MLflow, Weights & Biases, Ray, DVC

Evaluation & Testing

•      Prompt injection handling, adversarial testing

•      Human-in-the-loop (HITL) evaluation workflows

•      Logging, tracing (OpenTelemetry, LangSmith)

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