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Job Requirements:

• Experience building GenAI applications, including RAG, multi-agent systems, fine-tuning, etc., with tools such as Lang Chain, Lang Graph 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, Py Torch, 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.

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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.

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