GenAI Solution Architect

About the Role

• Architect AI Solutions:

  Design and build scalable, efficient, and secure GenAI systems leveraging advanced ML frameworks and LLMs.

• Framework Implementation:

  Lead the development of complex GenAI models using

  Python, TensorFlow, PyTorch

  , and related technologies.

• Model Optimization:

  Oversee optimization for performance, scalability, and cost-efficiency across training and inference pipelines.

• API Development:

  Guide the design of

  high-performance APIs (FastAPI or similar)

  for seamless integration with enterprise applications.

• MLOps Integration:

  Implement automated

  MLOps pipelines

  for deployment, monitoring, and scaling of models in production.

• CI/CD & Automation:

  Build automated CI/CD workflows ensuring smooth and frequent production releases.

• DevOps Practices:

  Deploy and manage AI models in production using

  Docker, Kubernetes, Jenkins, Terraform

  .

• Security & Compliance:

  Ensure systems meet data security, privacy, and compliance standards.

• Leadership & Mentorship:

  Provide technical guidance, mentor engineering teams, and make key architectural decisions.

• Cross-Team Collaboration:

  Partner with Data Science, Data Engineering, DevOps, and Product teams to deliver business-aligned solutions.

Key Requirements

• Experience:

  10+ years overall IT experience, with proven expertise in

  AI/GenAI architecture & solutioning

  .

• Programming Skills:

  Strong proficiency in

  Python, Pandas, NumPy

  .

• AI/ML Frameworks:

  Hands-on experience with

  TensorFlow, PyTorch

  , or similar.

• API Development:

  Proven expertise in

  FastAPI

  or equivalent frameworks.

• Software Engineering:

  Strong grounding in coding best practices, version control (

  Git

  ), automated testing, and CI/CD.

• MLOps:

  Deep knowledge of

  model deployment, monitoring, scaling, and optimization

  workflows.

• DevOps Tools:

  Experience with

  Docker, Kubernetes, Jenkins, Terraform

  for automation and infrastructure management.

• Leadership:

  Demonstrated ability to lead teams, mentor engineers, and drive architectural strategy.