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 servicesImplement end-to-end machine learningpipelines from data ingestion to training and model serving  Operationalize LLMs, embeddings, andmulti-agent systems in real-world applicationsManage 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 productionContainerize applications using Docker andorchestrate them via KubernetesBuild and maintain CI/CD pipelines for MLmodels and LLM applicationsDesign and implement production grade RAGsystemsAdvanced proficiency in Python programming with a focus on writing clean, testable and efficient codeDevOps & Containers: Proficient with Docker for containerization and working knowledge of Kubernetes (k8s) for orchestrationPractical understanding of GPU architecture and cloud compute instances to optimize resource allocation for training and inference workloadsMLOPS tools: hands on experience with MLflow (or similar tools like weights & biases) for experiment tracking and model registryProven experience working with Large Language Models (LLMs)Good understanding of AI agents & agentic workflows, LLM orchestration frameworks and reasoning patternsExperience with data preprocessing, feature engineering, and model selection and evaluation techniquesHands-on experience with CI/CD pipelines (GitLab, Jenkins)