Contract Duration: 3 Months
Engagement: Senior-Level Consulting / Contract
Workstreams: Module A & Module B
About the Role
We are looking for an experienced MLOps Engineer to support the delivery of a production-grade machine learning and data platform.
The successful candidate will be responsible for the infrastructure and operationalisation of multiple machine learning models, with a strong focus on low-latency model serving, database optimisation, streaming pipelines and production ML workloads.
The role requires someone who can work across both traditional machine learning infrastructure and graph data engineering.
Key Responsibilities
- Design and implement infrastructure supporting multiple machine learning models.
- Support multi-model deployment and model-serving environments.
- Build and manage containerised ML workloads using Docker.
- Develop and maintain workflow orchestration using Airflow.
- Optimise database performance and data access patterns.
- Design and support low-latency model-serving architectures.
- Build and maintain streaming and near-real-time data pipelines.
- Work with technologies such as Kafka, Redis or equivalent platforms.
- Support online feature/data stores and real-time decisioning requirements.
- Integrate ML pipelines with graph data and graph analytics workloads.
- Monitor model-serving performance, reliability and resource utilisation.
- Implement appropriate logging, monitoring and operational controls.
- Collaborate with the Solution/Data Architect, ML Lead and Data Scientist.
- Support the transition of models and data pipelines into production.
- Troubleshoot performance, scalability and infrastructure issues.
Required Experience & Skills
- Proven experience in MLOps, ML Engineering, Data Engineering or a closely related field.
- Strong experience supporting multiple machine learning models in production.
- Hands-on experience with Docker and Airflow.
- Strong database optimisation and performance-tuning experience.
- Experience with low-latency model serving.
- Experience building streaming or near-real-time data pipelines.
- Hands-on experience with Kafka, Redis or equivalent technologies.
- Strong understanding of machine learning lifecycle management.
- Experience with production data pipelines and distributed systems.
- Knowledge of graph data engineering.
- Strong Python skills.
- Ability to work across architecture, data engineering and machine learning teams.
Advantageous
- Experience in financial services, banking or fraud detection.
- Experience with Neo4j, NetworkX or other graph technologies.
- Experience with Kubernetes and cloud platforms.
- Experience supporting real-time fraud detection or transaction monitoring.
- Experience with model monitoring and drift management.
Engagement
This is a 3-month senior-level engagement requiring hands-on involvement throughout the project.
Ideal candidate: A technically strong MLOps professional who understands both the infrastructure and data requirements needed to operate high-performance machine learning models in production.