As an MLOps (Machine Learning Operations) Engineer, you will be responsible for applying DevOps principles to the machine learning lifecycle, bridging the gap between data science and IT operations. You will design, build, and maintain the infrastructure and automated pipelines that allow machine learning models and complex bioinformatics workflows to be trained, benchmarked, deployed, and monitored efficiently and reliably for internal RD purposes and production.
In this role, you will have the opportunity to:
- Develop deep expertise to code, debug, and optimize complex Valohai workflows. Implement and debug workflow scripts per data scientist specs (e.g. in NextFlow, Argo, Valohai, etc environments).
- Manage Azure environment. Also will have responsibility of working with IT and Privacy Security team to manage the envirnoments. Manage scalable infrastructure for ML workloads using cloud platforms, containerization (Docker), container orchestration (Kubernetes), and Valohai configuration. Code in Python to optimize and debug pipelines.
- Collaborate with data scientists and machine learning engineers to ensure models are production-ready and to manage model versions and artifacts. Deploy models into production, e.g. using REST APIs, and manage their lifecycle from staging to production.
- Develop and implement CI/CD pipelines specifically for machine learning models, automating the entire workflow from training and testing to deployment and monitoring. Establish and maintain a robust monitoring and observability framework for deployed models, tracking key metrics like accuracy, latency, and data drift.
- Ensure the reliability, security, and scalability of all ML systems in production. Implement version control for data, code, and models to ensure reproducibility and governance. Troubleshoot and optimize performance for distributed systems and AI workloads, including GPU utilization.
Essential requirements of the job include:
- Bachelor s or Master degree in Computer Science, Engineering, Information Technology, or a related field with an experience of 8+ years in DevOps and MLOps
- Proven experience as an MLOps, DevOps, or ML platform engineer, with experience in deploying and managing ML models. Proficiency in programming languages like Python and familiarity with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Strong experience with cloud platforms such as AWS, Google Cloud (GCP), or Azure. Hands-on experience with containerization technologies, such as Docker, and orchestration platforms like Kubernetes.
- Experience with CI/CD tools (e.g., GitHub Actions, Jenkins, GitLab CI) and automation. Familiarity with ML lifecycle management tools such as MLflow, Kubeflow, or SageMaker. Understanding of data engineering concepts, including ETL processes and data pipeline orchestration. Familiarity with monitoring, logging, and alerting tools for production systems
It would be a plus if you also possess previous experience in:
- Model observability and experiment tracking (e.g., MLflow, Weights Biases, Kubeflow).Infrastructure as Code and environment reproducibility (e.g., Terraform, Helm).
- Security and compliance practices for ML systems (e.g., secrets management, least-privilege access, secure endpoints). GenAI/LLM operations, including RAG pipelines, vector databases, and model serving frameworks
Join our winning team today. Together, we ll accelerate the real-life impact of tomorrow s science and technology. We partner with customers across the globe to help them solve their most complex challenges, architecting solutions that bring the power of science to life.
For more information, visit www.danaher.com .
