Machine Learning Engineer

We are seeking a skilledmachine learning platform engineer (MLOps) to join our agile platform teamwhich is part of our ML & AI ART. You drive the orchestration of advancedagentic workflows to enable autonomous, AI-driven systems. You will beresponsible for engineering robust data pipelines, establishing comprehensivemodel management lifecycles, overseeing all foundational platform-level AIintegrations – including engineering a robust library of AI skills for agentuse. 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/CD pipelines for MLmodels and LLM applications • Design and implement production grade RAGsystems • Advanced proficiency in Python programming with a focus on writing clean, testable and efficient code • DevOps & Containers: Proficient with Docker for containerization and working knowledge of Kubernetes (k8s) for orchestration • Practical understanding of GPU architecture and cloud compute instances to optimize resource allocation for training and inference workloads • MLOPS tools: 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)