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AI Engineer

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Remote

AI Engineer

Etech

AI Engineer

Skills

DockerPythonTesting & QAPyTorchMachine LearningAINLP / Natural Language ProcessingFastAPIApache Airflow

2 months ago

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Important information


Contract type:

Permanent

Location:

Gandhinagar, Gujarat, India

Work mode:

Remote

Published on:

1 September 2026

What they need


Title: AI Engineer

**Job Location:**Gandhinagar
**Job Mode:**Remote
Shift Time: 2:30 PM to 11:30 PM IST

Role Summary

Were looking for a highly motivated AI Engineer with 3–5, good to have years of experience to help us build LLM-powered applications. This role is fully remote and will focus on using tools like Lang Chain, Lang Graph, and open-source LLMs such as Ollama, LLaMA, Mistral, or Phi. You'll contribute to developing document understanding systems, intelligent chat agents, and multi-agent orchestration workflows.

What you will be doing:

  • Design, build, and deploy LLM-driven applications (e.g., document summarization, RAG-based QA, chatbots).
  • Work with open-source LLMs using platforms like Ollama and Hugging Face.
  • Implement LangChain and LangGraph workflows for multi-step, multi-agent task resolution.
  • Build and optimize RAG (Retrieval-Augmented Generation) systems using vector databases.
  • Collaborate with cross-functional teams to ship features to production.
  • Stay up-to-date with the latest in open-source LLMs, model optimization (LoRA, quantization), and multi-modal AI.

Required Skills

  • 3–5 years of hands-on experience in AI/ML engineering.
  • Proficient in Python, PyTorch, and Hugging Face Transformers.
  • Proven experience with LangChain and LangGraph for LLM workflows.
  • Familiarity with Ollama, Mistral, LLaMA, or similar open-source LLMs.
  • Experience working with vector stores (Qdrant, Pinecone, Weaviate, FAISS).
  • Skilled in backend integration using FastAPI, Docker, and cloud platforms.
  • Solid grasp of NLP, LLM reasoning, prompt engineering, and document parsing.

Nice-to-Have

  • Experience with LangServe, OpenAI tool/function calling, or agent orchestration.
  • Background in multi-modal AI (e.g., image + text analysis).
  • Familiarity with MLOps tools (MLflow, Weights & Biases, Airflow).
  • Contributions to open-source GenAI projects.
  • Understanding of LLM safety, security, and alignment principles.

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