Senior MLOps Engineer; AWS Services

We are seeking a Senior MLOps Engineer (AWS Services) to manage the full lifecycle of machine learning models, from development through deployment and ongoing monitoring. The person in this role will apply MLOps principles to ensure robust, scalable and reliable production systems, and will design and operate distributed data processing pipelines capable of handling large volumes of structured and unstructured data in near real-time.

This role requires deep expertise in AWS services.

• Manage the end-to-end lifecycle of machine learning models: development, testing, deployment, monitoring and maintenance.
• Design, build and operate CI/CD pipelines for machine learning artifacts, ensuring automated testing, reproducibility and traceability.
• Implement robust monitoring, alerting and performance optimisation for models and data pipelines in production.
• Architect and develop distributed, highly parallelised big data processing pipelines that handle large-scale structured and unstructured data in near real-time.
• Leverage AWS services (compute, storage, data processing, orchestration and monitoring) to deliver scalable and secure MLOps solutions.
• Collaborate with data scientists, Dev Ops engineers, IT and security teams to integrate models into enterprise environments and ensure compliance with operational requirements.
• Define and enforce best practices for model versioning, data management, reproducibility and governance.
• Conduct performance tuning, capacity planning and cost optimisation for cloud resources supporting ML workloads.
• Provide technical leadership, mentorship and knowledge sharing across cross‑functional teams.

• 10+ years of professional experience with AWS services
  , including hands‑on implementation of cloud‑based data and ML solutions.
• Proven experience delivering projects for the banking sector or other highly regulated industries, with an understanding of security, compliance and operational requirements.
• Strong proficiency with MLOps practices: CI/CD for models, automated testing, model monitoring and deployment patterns.
• Experience building distributed data processing systems and pipelines for both structured and unstructured data; familiarity with near real‑time processing architectures.
• Practical knowledge of relevant AWS services such as EC2, ECS/EKS, Lambda, S3, Glue, EMR, Sage Maker, Kinesis, MSK, RDS/Aurora, IAM and Cloud Watch or equivalents.
• Solid programming skills in languages commonly used for ML and data engineering (for example Python, and familiarity with frameworks and tools such as Terraform, Docker, Kubernetes, Spark).
• Experience with security controls, data governance, and operational resilience in cloud environments.
• Bachelor’s degree in Computer Science, Engineering, Data Science or a related discipline, or equivalent practical experience.
• Strong communication skills and the ability to coordinate work across multidisciplinary teams.
• Opportunities for professional development and technical growth within advanced data and cloud projects.
• Collaboration with experienced cross‑functional teams on complex, large‑scale initiatives in the banking domain.
• Access to modern cloud infrastructure and tools for experimentation and production deployment.
• Support for continuous learning, including technical training and knowledge sharing.
• Unique TEAL culture: relationship‑ and respect‑driven community in a non‑corporate atmosphere.
• Agile working model with minimal bureaucracy.
• Company integration trips to various locations in Europe for all employees.
• Activities supporting employee well‑being and health.
• Luxmed Gold Extended medical care and Multisport Plus benefit.