Job Description

Role Purpose

The purpose of this role is to work with Application teams and developers to facilitate better coordination amongst operations, development and testing functions by automating and streamlining the integration and deployment processes

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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

• 5+ 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:

• 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

• Build bespoke AI and transformative agentic AI solutions

• Technical debugging and root cause analysis

• Rapid prototyping

• Implement and administer best practices

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