Generative AI Architect PAN India

Job Requirements

• Candidate should have 15+ years of experience.
• Experience building GenAI applications, including RAG, multi-agent systems, fine-tuning, etc., with tools such as LangChain, LangGraph etc.
• Clear understanding of Model Context Protocols, A2A Protocols, Agent Developeer Kit and working experience with LLMs
• Expertise in deploying production grade GenAI solutions, including evaluation and optimizations; Machine Learning deployments on AWS, Azure or GCP
• Extensive hands-on data science experience, leveraging machine learning and data science tools (i.e., pandas, scikit-learn, PyTorch, etc.)
• Experience with DevOps tools: Kubernetes, Docker, Terraform, CI/CD Pipelines, GitHub/GitLab, GitOps, GitHub Actions, Jira, Jenkins, CircleCI, Datadog, Slack.
• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
• Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike
• 15+ years of engineering and technical deployment experience in a customer-facing set up
• Should scoped and delivered complex systems in rapid and ambiguous environments
• Delivered production-grade code across frontend and backend using Python, JavaScript, or similar stacks
• Understand how AI model behaviour affects product experience
• Communicate clearly with engineers, product teams, and customer stakeholders
• Flag risks early and seek attention as per the severity

Key responsibilities:

• Work with go-to-market teams to win clients for AI platform / agent offerings
• Help clients integrate and adopt the offerings; demonstrate the impact / outcomes the offerings commited such as KPI improvements and help client succeed
• Embed within the client landscape, understand their domain and co-develop solutions with the core product engineering teams
• Own technical delivery across multiple deployments from prototype to stable release
• Drive product roadmap and make decisions on scope and priorities
• Develop deployment architectures for cloud or on-prem installation
• Systemize working patterns into tools, playbooks, or reference frameworks/templates for sharing across client and product engineering teams

Implement and administer best practices