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GenAI Developer

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Hybrid

GenAI Developer

Elabs Infotech

GenAI Developer

Skills

Google Cloud PlatformDockerFlaskPythonMachine LearningAIAWSKubernetesFastAPIAzure

9 days ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Hybrid

Published on:

17 October 2026

What they need


Experience: 520 Years, depending on level
Location: PAN India / Project Location
Notice Period: Immediate to 30 Days preferred

Job Description:

We are seeking skilled GenAI Developers to design, build, and deploy AI-powered applications using Large Language Models, Retrieval-Augmented Generation (RAG), and modern AI frameworks. Candidates should have hands-on experience in Python, LLM application development, and at least one cloud platformAWS, Azure, or GCP.

Key Responsibilities:

  • Develop and deploy Generative AI applications using LLMs.
  • Design and implement RAG pipelines for enterprise use cases.
  • Build AI agents and agentic workflows using frameworks such as LangChain and LangGraph.
  • Integrate LLMs with APIs, databases, vector stores, and enterprise systems.
  • Develop scalable backend services and REST APIs using Python.
  • Optimize prompts, retrieval strategies, and LLM application performance.
  • Deploy AI solutions on AWS, Azure, or GCP.
  • Implement CI/CD, Docker, Kubernetes, monitoring, and MLOps practices as required.
  • Collaborate with engineering, product, and business teams to deliver production-ready AI solutions.

Mandatory Skills:

  • Generative AI and Large Language Models (LLMs)
  • Retrieval-Augmented Generation (RAG)
  • LangChain and/or LangGraph
  • Python programming
  • Machine Learning fundamentals
  • Hands-on experience with at least one cloud platform: AWS, Azure, or GCP

Good to Have:

  • Agentic AI and Model Context Protocol (MCP)
  • LLM fine-tuning and prompt engineering
  • Vector databases such as Pinecone, FAISS, Chroma, or Qdrant
  • FastAPI / Flask and REST API development
  • Docker, Kubernetes, CI/CD, and MLOps
  • Production deployment, monitoring, and LLM evaluation

Level-Based Expectations:

  • Developer: Hands-on implementation of LLM, RAG, and GenAI applications.
  • Lead: Technical ownership, solution delivery, code reviews, and team coordination.
  • Architect: End-to-end GenAI architecture, cloud solution design, scalability, security, governance, and technical leadership.

Interested candidates can share their updated resumes along with total experience, relevant GenAI experience, current location, notice period, and current CTC/expected CTC.

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