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Job Requirements
- 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
- 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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