Machine Learning Engineer

Contract Length 12-month contract (with potential extension) Location Fully Remote Industry IT Consulting & Services Role Overview We are seeking an experienced Machine Learning Engineer to support enterprise clients within a hybrid cloud environment, combining on-premise infrastructure with public cloud platforms. The role sits within a global IT Consulting & Services organization delivering scalable, secure, and production-grade AI solutions. You will focus on designing, deploying, and operating machine learning systems that integrate seamlessly across hybrid cloud architectures, ensuring performance, reliability, and compliance. This is a hands-on delivery role with a strong emphasis on production ML and MLOps rather than research-only work. Key Responsibilities Machine Learning & AI Development • Design, build, train, and optimize machine learning models for enterprise use cases • Translate business and client requirements into deployable ML solutions • Work with structured and unstructured data (tabular, text, logs, time-series) • Evaluate model performance and drive continuous improvement Hybrid Cloud Deployment & MLOps • Deploy and manage ML models across hybrid cloud environments (on-prem + AWS/Azure/GCP) • Build and maintain end-to-end ML pipelines for training, validation, deployment, and monitoring • Implement scalable MLOps practices using containerization and orchestration tools • Monitor models for drift, performance issues, and operational health across environments Infrastructure & Integration • Work closely with cloud, platform, and networking teams to integrate ML solutions into existing enterprise systems • Ensure ML solutions meet security, compliance, and data residency requirements • Support integration with data platforms, APIs, and downstream applications Collaboration & Consulting • Collaborate with data scientists, software engineers, cloud architects, and client stakeholders • Contribute to solution architecture and technical design discussions • Produce clear technical documentation and support knowledge sharing across teams Required Skills & Experience Machine Learning & Programming • Strong experience using Python for machine learning and data processing • Solid understanding of machine learning algorithms and model evaluation techniques • Hands-on experience with PyTorch and/or TensorFlow • Proven experience deploying ML models into production environments Hybrid Cloud & MLOps • Experience working in hybrid cloud architectures • Strong experience with Docker and Kubernetes (on-prem and/or managed services) • Experience with CI/CD pipelines for ML workflows • Hands-on experience with at least one public cloud platform (AWS, Azure, or GCP) • Familiarity with ML lifecycle and MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML) Data & Systems • Strong SQL skills and experience working with large datasets • Understanding of data pipelines and data engineering fundamentals Desirable / Nice-to-Have Skills • Experience with LLMs, NLP, or Generative AI solutions • Experience supporting regulated or security-sensitive environments • Consulting or client-facing delivery experience • Knowledge of model governance, explainability, and responsible AI practices