Teams at MUTT DATA

Lead end-to-end data migration initiatives across cloud data platforms, including architecture decisions, dependency mapping, cutover planning, validation, risk mitigation, and stakeholder alignment. Build and optimize batch and streaming data pipelines using Databricks, Python, and Spark. Maintain migration documentation, testing strategies, rollback plans, and operational procedures while promoting data governance, quality, and delivery best practices.

Build and deploy LLM-based agents for banking clients using LangGraph, LiteLLM, and Langfuse. Develop backend services, API integrations, guardrails, evaluations, observability, and production infrastructure on AWS. Collaborate with security, networking, DevSecOps, and business teams to resolve delivery issues. Create documentation and runbooks, transfer ownership to client teams, and improve reusable agent templates, deployment pipelines, and platform observability after each embedded engagement.

Industrialize, deploy, monitor, and scale machine learning models in production. Build training, inference, retraining, CI/CD, model tracking, and model-serving pipelines. Use MLflow for versioning and governance, Databricks for workflow orchestration, Docker and Kubernetes for deployment, and observability practices for reliable ML operations. Collaborate with data scientists, data engineers, and business stakeholders to deliver scalable, production-grade ML systems.