Design, develop and deploy machine learningsolutions and services
Implement end-to-end machine learningpipelines from data ingestion to training and model serving
Operationalize LLMs, embeddings, andmulti-agent systems in real-world applications
Manage the machine learning and modellifecycle (experimentation, registry, deployment)
Oversee the model promotion lifecycle,coordinating validation gates and approval workflows to safely deploy new modelversions from stating to production
Containerize applications using Docker andorchestrate them via Kubernetes
Build and maintain CI/CDpipelines for ML models and LLM applications
Advanced proficiency in Python programming with a focus on writing clean, testable and efficient code
Proficient with Docker for containerization and working knowledge of Kubernetes for orchestration
Practical understanding of GPU architecture and cloud compute instances to optimize resource allocation for training and inference workloads
Hands on experience with MLflow (or similar tools like weights & biases) for experiment tracking and model registry
Proven experience working with Large Language Models (LLMs)
Good understanding of AI agents & agentic workflows, LLM orchestration frameworks and reasoning patterns
Experience with data preprocessing, feature engineering, and model selection and evaluation techniques
Hands-on experience with CI/CD pipelines (GitLab, Jenkins)
