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Job Type

Full Time

Job Description

AI Architect


Primary Stack & Tools:

  • Languages

    : Python, SQL, Bash
  • ML/AI Frameworks

    : PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers
  • GenAI & LLM Tooling

    : OpenAI APIs, LangChain, LlamaIndex, Cohere, Claude, Azure OpenAI
  • Agentic & Multi-Agent Frameworks

    : LangGraph, CrewAI, Agno, AutoGen
  • Search & Retrieval

    : FAISS, Pinecone, Weaviate, Elasticsearch
  • Cloud Platforms

    : AWS, GCP, Azure (preferred: Vertex AI, SageMaker, Bedrock)
  • MLOps & DevOps

    : MLflow, Kubeflow, Docker, Kubernetes, CI/CD pipelines, Terraform, FAST API
  • Data Tools

    : Snowflake, BigQuery, Spark, Airflow


Key Responsibilities:

  • Architect scalable and secure AI systems leveraging

    LLMs

    ,

    GenAI

    , and

    multi-agent frameworks

    to support diverse enterprise use cases (e.g., automation, personalization, intelligent search).
  • Design and oversee implementation of

    retrieval-augmented generation (RAG)

    pipelines integrating vector databases, LLMs, and proprietary knowledge bases.
  • Build robust

    agentic workflows

    using tools like

    LangGraph

    ,

    CrewAI

    , or

    Agno

    , enabling autonomous task execution, planning, memory, and tool use.
  • Collaborate with product, engineering, and data teams to translate business requirements into architectural blueprints and technical roadmaps.
  • Define and enforce

    AI/ML infrastructure best practices

    , including security, scalability, observability, and model governance.
  • Manage technical road-map, sprint cadence, and 3–5 AI engineers; coach on best practices.
  • Lead AI solution design reviews and ensure alignment with compliance, ethics, and responsible AI standards.
  • Evaluate emerging GenAI & agentic tools; run proofs-of-concept and guide build-vs-buy decisions.


Qualifications:

  • 10+ years of experience in AI/ML engineering or data science, with 3+ years in AI architecture or system design.
  • Proven experience designing and deploying

    LLM-based solutions

    at scale, including

    fine-tuning

    ,

    prompt engineering

    , and

    RAG-based systems

    .
  • Strong understanding of

    agentic AI design principles

    ,

    multi-agent orchestration

    , and

    tool-augmented LLMs

    .
  • Proficiency with cloud-native ML/AI services and infrastructure design across AWS, GCP, or Azure.
  • Deep expertise in model lifecycle management, MLOps, and deployment workflows (batch, real-time, streaming).
  • Familiarity with

    data governance

    ,

    AI ethics

    , and

    security considerations

    in production-grade systems.
  • Excellent communication and leadership skills, with the ability to influence technical and business stakeholders.

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