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)