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Support the development of a Python-based enterprise data hub (integrated with Oracle) and advance the MLOps infrastructure. This role combines DevOps excellence with hands-on machine learning engineering to deliver scalable, reliable, and auditable ML solutions. Key objectives include automating CI/CD pipelines for data and ML workloads, accelerating model deployment, ensuring system stability, enforcing infrastructure-as-code, and maintaining secure, compliant operations.YOUR CHALLENGE- Design and maintain CI/CD pipelines for Python applications and machine learning models using GitLab CI/Jenkins, Docker, and Kubernetes
- Develop, train, and evaluate machine learning models (e.g., using scikit-learn, XGBoost, PyTorch) in close collaboration with data scientists
- Orchestrate end-to-end ML workflows including pre-processing, training, hyperparameter tuning, and model validation
- Deploy and serve models in production using containerised microservices (Docker/K8s) and REST/gRPC APIs
- Manage the MLOps lifecycle via tools like MLflow (experiment tracking, model registry) and implement monitoring for drift, degradation, and performance
- Refactor exploratory code (e.g., Jupyter notebooks) into robust, testable, and version-controlled production pipelines
- Collaborate with data engineers to deploy and optimise the data hub, ensuring reliable data flows for training and inference
- Troubleshoot operational issues across infrastructure, data, and model layers; participate in incident response and root cause analysis
- Technical Proficiency: Strong skills in Python, Linux, CI/CD, Docker, Kubernetes, and MLOps tools (e.g., MLflow). Practical experience with Oracle databases, SQL, and ML frameworks
- ML Engineering Aptitude: Ability to own the full ML lifecycle—from training and evaluation to deployment and monitoring—with attention to reproducibility and compliance
- Automation & Reliability: Committed to building stable, self-healing systems with proactive monitoring and automated recovery
- Collaboration & Communication: Effective team player in agile, cross-functional settings; able to communicate clearly across technical and non-technical audiences
Education and Skills Requirements
- Education: Bachelor of Science (BS) in Computer Science, Engineering, Data Science, or related field. Certifications such as CKA, AWS/Azure DevOps Engineer, or Google Cloud Professional DevOps Engineer are a plus
Technical Skills:
- Proficient in Python, Git, and shell scripting
- Experienced with CI/CD pipelines (GitLab, Jenkins), Docker, and Kubernetes
- Skilled in SQL and Oracle database interactions
- Hands-on with MLOps frameworks (e.g., MLflow), model deployment, and monitoring
- Familiarity with microservices, REST/gRPC, and basic ML model evaluation techniques
Experience:
- Minimum 5 years in DevOps, SRE, or ML Engineering roles, with at least
- 2–3 years focused on data-intensive or machine learning systems
- Experience in financial services or regulated environments is highly valued
Languages:
- English is a must
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