GenAI Architect

GenAI Architect

Key Responsibilities:

• Architect and implement advanced

  Generative AI

  models for text and image generation, RAG pipelines, prompt engineering, and synthetic data generation.
• Design

  RAG architecture

  ,

  Agentic AI frameworks

  , and

  inference endpoints

  ensuring scalability and observability.
• Develop, fine-tune, and optimize LLMs (OpenAI, Azure OpenAI, open-source models) for business applications.
• Apply

  advanced prompt engineering techniques

  to improve AI accuracy, interpretability, and efficiency.
• Integrate generative AI models into existing applications using

  Python

  ,

  Docker

  ,

  Kubernetes

  , and

  LangChain

  .
• Implement

  data security

  ,

  responsible AI

  ,

  observability

  , and

  traceability

  across the AI pipeline.
• Collaborate with data engineers and developers to align model outcomes with product and business objectives.
• Evaluate and compare multiple LLMs using structured benchmarking and performance metrics.
• Stay ahead of industry trends in

  GenAI

  ,

  RAG

  , and

  Agentic AI

  , driving adoption of new best practices.

• bachelors or masters degree in

  Computer Science, AI/ML, Data Science, or related fields

  .
• 7 10 years total experience with at least 2 years in

  Generative AI

  solutioning.
• Strong proficiency in

  Python

  with experience in

  TensorFlow

  ,

  PyTorch

  , and

  LangChain

  .
• Hands-on experience with

  Azure AI Foundry

  ,

  AWS Bedrock

  ,

  Azure AI Search

  ,

  Prompt Flow

  , and

  Inference Endpoints

  .
• Deep understanding of

  RAG pipelines

  ,

  prompt engineering

  , and

  zero-shot learning

  .
• Strong background in

  data science principles

  ,

  statistical analysis

  , and

  model performance tuning

  .

• Experience building and deploying

  Agentic AI frameworks

  and

  LLM orchestration pipelines

  .
• Familiarity with

  NLP

  or

  Computer Vision

  for Generative AI applications.
• Knowledge of

  Kubernetes

  ,

  Docker

  , and

  cloud-native AI

  deployment practices.
• Awareness of

  ethical AI

  ,

  data security

  , and

  governance frameworks

  .