Senior AI/ML Engineer (ID:3418)

As a Senior AI/ML Engineer, you will: Design, develop, deploy, and operate production-grade machine learning systems across multiple use cases such as recommendations, forecasting, and automation. Build and maintain end-to-end ML pipelines covering training, validation, deployment, monitoring, and lifecycle management. Focus on ML Ops and ML Platform development, ensuring scalable, reliable, and maintainable ML workflows. Collaborate closely with Data Scientists, Engineers, and Product Managers to productionise ML models and research code. Develop and expose ML models as scalable APIs and services using tools such as FastAPI. Automate model training and deployment using CI/CD pipelines (GitHub Actions, Azure DevOps). Improve observability, reliability, and performance of ML systems through logging, monitoring, and alerting. Implement model monitoring and drift detection using tools such as Azure Monitor, NewRelic, and custom frameworks. Manage and continuously improve ML infrastructure using Terraform, Docker, and container-based platforms. Work with orchestration tools such as Airflow or Azure ML to manage ML workflows. What You Bring to the Table: 6–8 years of experience in ML Engineering, Data Engineering, or DevOps roles with exposure to the full ML lifecycle. Strong proficiency in Python, with working knowledge of SQL and Bash. Hands-on experience with ML frameworks and tools such as MLflow, Scikit-learn, and/or PyTorch. Experience building and maintaining ML pipelines and workflows. Practical experience with cloud platforms, particularly Azure and AWS. Strong understanding of containerisation and orchestration, including Docker and Kubernetes. Experience with CI/CD tools such as GitHub Actions and Azure DevOps. Familiarity with data platforms such as Snowflake, Delta Lake, Redis, and Azure Data Lake. You Should Possess the Ability to: Translate ML research and prototypes into scalable, production-ready systems. Design and operate reliable ML pipelines with strong observability and monitoring. Automate deployment and operational workflows to improve speed and reliability. Troubleshoot and optimise ML systems for performance and scalability. What We Bring to the Table: Opportunity to work on end-to-end ML platforms and production ML systems. Exposure to modern ML Ops tooling and cloud-native architectures. A collaborative environment focused on scalable, reliable, and high-impact ML solutions. Hands-on experience with advanced monitoring, automation, and infrastructure practices. Continuous learning through real-world application of ML, cloud, and DevOps technologies. Let’s Connect Want to discuss this opportunity in more detail? Feel free to reach out. Recruiter: Asha Krishnan Phone: +31 20 369 0609 ; Extn :132 Email: asha.k@stafide.nl LinkedIn: https://www.linkedin.com/in/asha-krishnan