AI/ML Architect – Generative AI

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About the Role

AI/ML Architect

Key Responsibilities

• Generative AI Development:

  Build and test agentic and non-agentic AI solutions using LangGraph, Autogen, or CrewAI.

• LLM Solutions:

  Apply advanced RAG, RLHF, and prompt engineering techniques to design intelligent solutions.

• Data & Model Ops:

  Develop pipelines for data preprocessing, prompt management, caching, and validation.

• Solution Design:

  Architect end-to-end Generative AI solutions for enterprise use cases across support, sales, digital, chat, and voice.

• Prototyping:

  Develop scalable prototypes of LLM/NLP systems critical to business operations.

• Research & Innovation:

  Apply state-of-the-art LLM techniques to analyze large unstructured datasets.

• Deployment:

  Operationalize solutions using

  LLMOps

  with cloud deployment on AWS, GCP, or Azure.

• Collaboration:

  Partner with business and process experts to design AI-driven solutions.

Required Qualifications

• Minimum 4 years of experience in

  Data Science, LLM, or Generative AI

  .
• Master’s degree in Computer Science, AI, or a related field (preferred).
• Proven experience delivering

  LLM solutions

  to real-world business problems.
• Proficiency in

  Python, Jupyter Notebooks, Node.js

  .
• Experience with

  LangChain, LangGraph, RAG pipelines, and vector databases

  .
• Hands-on expertise with

  NLP, Transformers, Neural Networks, and embedding models

  .
• Familiarity with

  LLM frameworks

  (OpenAI, Gemini, Vertex AI, Mistral, LLaMA, etc.).
• Strong communication skills with the ability to explain technical concepts to non-technical audiences.

Nice to Have

• Experience with

  Docker, Agile methodologies

  , and cloud deployments (AWS/GCP).
• Exposure to

  automation, AI-driven operations, and success measurement frameworks

  .
• Ability to research and recommend

  best practices for scalable AI/ML systems

  .