Design, develop and deploy machine learning solutions and services.
Implement end-to-end machine learning pipelines from data ingestion to training and model serving.
Operationalize LLMs, embeddings, and multi-agent systems in real-world applications.
Manage the machine learning and model lifecycle (experimentation, registry, deployment).
Oversee the model promotion lifecycle, coordinating validation. gates and approval workflows to safely deploy new model versions from stating to production.
Containerize applications using Docker and orchestrate them via Kubernetes.
Build and maintain CI/CD pipelines for ML models and LLM applications.
Collaborate with data scientists to refactor research code into production-ready Python code.
Monitor model performance, data drift, and performance in production.
Assess and integrate AI solutions ensuring optimal performance and reliability.
Design and implement production grade RAG systems.
Collaborate with infrastructure teams, data engineers, data scientists, and other stakeholders to integrate machine learning solutions into existing systems and processes.
Participate in code reviews, testing, and debugging to ensure the quality and reliability of machine learning solutions.
