Gen AI Architect

Area(s) of responsibility

Job Title: Generative AI Technical Architect

Role Overview

Generative AI Architect will design, develop, and implement advanced generative AI solutions that drive business impact. This role offers the opportunity to work at the forefront of AI innovation.

The Architect will lead the end-to-end architecture, design, and deployment of scalable generative AI systems. Responsibilities include conceptualizing solutions, selecting models/frameworks, overseeing development, and integrating AI capabilities into platforms. Collaboration with stakeholders to translate complex requirements into high-performance AI solutions is key.

Key Responsibilities

• Design GenAI Solutions: Lead architecture of generative AI systems, including LLM selection, RAG, and fine-tuning.
• Azure AI Expertise: Build scalable AI solutions using Azure AI services.
• Python Development: Write efficient, maintainable Python code for data processing, automation, and APIs.
• Model Optimization: Enhance model performance, scalability, and cost-efficiency.
• Data Strategy: Design data pipelines for training/inference using Azure data services.
• Integration & Deployment: Integrate models into enterprise systems. Implement MLOps, CI/CD (Azure DevOps, GitHub, Jenkins), and containerization (Docker, Kubernetes).
• Technical Leadership: Guide teams on AI development and deployment best practices.
• Innovation: Stay updated on GenAI trends. Drive PoCs and pilot implementations.
• Collaboration: Work with cross-functional teams to align AI solutions with business goals. Communicate technical concepts to non-technical stakeholders.
  
  

• 12–16 years in IT, with 3+ years in GenAI architecture.
• Technical Proficiency:
• Azure AI services
• Python, TensorFlow, PyTorch, Hugging Face, LangChain, LlamaIndex
• LLMs, transformers, diffusion models
• Prompt engineering, RAG, vector DBs (Pinecone, Weaviate, Chroma)
• MLOps, CI/CD, Kubernetes
• RESTful API development
• Architecture: Cloud, microservices, design patterns
• Problem-Solving: Strong analytical and creative thinking
• Communication: Clear articulation of complex concepts
• Teamwork: Agile collaboration and project leadership
  

• Azure AI certifications
• Experience with AWS
• LLM fine-tuning
• Open-source contributions or AI/ML publications