Generative AI Engineer

5 - 7 years

0 Lacs

Posted:20 hours ago| Platform: Foundit logo

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Work Mode

On-site

Job Type

Full Time

Job Description

Software Developer

Key Responsibilities

  • Design, develop, and deploy

    AI/ML solutions with a focus on Generative AI and LLMs

    .
  • Fine-tune, prompt-engineer, and optimize LLMs for domain-specific tasks.
  • Leverage frameworks such as

    LangChain, LlamaIndex, or similar

    for building modular AI applications.
  • Integrate LLMs with APIs, databases, vector stores, and enterprise applications.
  • Evaluate and implement

    model deployment strategies

    (on-prem, cloud, serverless, or containerized environments).
  • Collaborate with cross-functional teams (data engineers, product managers, software engineers) to deliver end-to-end AI solutions.
  • Stay updated with the latest advancements in

    AI, ML, and GenAI research

    and propose adoption of new technologies.
  • Implement

    MLOps best practices

    for continuous training, deployment, and monitoring of models in production.
  • Ensure

    responsible AI practices

    , focusing on bias mitigation, data privacy, and compliance.

Required Qualifications

  • 5+ years of experience

    in software development (Python preferred; familiarity with Java, C#, or Node.js a plus).
  • 2+ years of hands-on experience

    in Generative AI and LLM-based development.
  • Strong knowledge of

    AI/ML fundamentals

    including NLP, deep learning, embeddings, and transformers.
  • Experience with

    LLMs

    (e.g., OpenAI GPT, Anthropic Claude, LLaMA, Falcon, Mistral, or similar).
  • Proven track record of delivering

    at least 2 production-grade LLM-based projects

    .
  • Experience with

    LangChain, LlamaIndex, Haystack, or similar frameworks

    .
  • Familiarity with

    vector databases

    (Pinecone, Weaviate, Milvus, FAISS, Chroma).
  • Hands-on experience with

    cloud platforms

    (AWS, Azure, GCP) and AI services (Bedrock, VertexAI, Azure OpenAI).
  • Proficiency in

    containerization and deployment tools

    (Docker, Kubernetes, CI/CD pipelines).
  • Experience with

    MLOps tools

    (MLflow, Kubeflow, Weights & Biases) for model lifecycle management.
  • Strong problem-solving, analytical, and communication skills.

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