Architect

13 - 20 years

25 - 40 Lacs

Posted:19 hours ago| Platform: Naukri logo

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Work Mode

Hybrid

Job Type

Full Time

Job Description

  • Define and drive the

    end-to-end architecture

    for Generative AI solutions across enterprise use cases.
  • Design and implement

    RAG pipelines

    , integrating vector databases and retrieval frameworks for scalable AI search and reasoning.
  • Architect solutions leveraging

    LLMs/SLMs

    such as OpenAI GPT, Claude, GPT-4o, Ollama, LLaMA, Mistral, etc.
  • Lead

    prompt engineering strategies

    , fine-tuning, and optimization for specific business problems.
  • Build AI workflows using

    LangChain

    (or equivalent frameworks) for orchestration and application development.
  • Provide architectural governance for

    NLP-driven solutions

    (classification, summarization, conversational AI, knowledge retrieval).
  • Collaborate with data engineers, ML engineers, and product teams to define solution roadmaps.
  • Evaluate, benchmark, and recommend emerging AI tools, frameworks, and models.
  • Ensure

    security, compliance, and ethical AI practices

    in all deployments.

Required Skills

  • Proven expertise in

    Prompt Engineering

    and

    RAG architectures

    .
  • Hands-on experience with

    LLMs/SLMs

    (OpenAI, Claude, GPT-4o, Ollama, LLaMA, Mistral, etc.).
  • Strong knowledge of

    LangChain

    and other orchestration frameworks.
  • Deep understanding of

    NLP techniques

    , embeddings, and vector search.
  • Strong programming skills in

    Python

    and familiarity with

    AI/ML libraries

    (Transformers, Hugging Face, PyTorch, TensorFlow).
  • Experience designing

    scalable AI systems

    with microservices, APIs, and cloud-native deployment.
  • Strong understanding of

    vector databases

    (Pinecone, Weaviate, Chroma, Milvus).
  • Prior experience leading

    AI solution architecture

    in large-scale enterprise environments.

Good-to-Have Skills

  • Experience with

    cloud platforms

    (AWS, Azure, GCP) for AI deployments.
  • Familiarity with

    MLOps, CI/CD pipelines

    , and monitoring frameworks for AI models.
  • Knowledge of

    fine-tuning, model compression, and optimization techniques

    .
  • Exposure to

    data privacy, Responsible AI, and compliance frameworks

    .

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