Enigma
MLOps Engineer | Python | Airflow | AWS | MLFlow | Docker | Kubernetes | London, Hybrid
London
Posted 2 days ago
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MLOps Engineer | Python | Airflow | AWS | MLFlow | Docker | Kubernetes | London, Hybrid
MLOps Engineer | Python | Airflow | AWS | MLFlow | Docker | Kubernetes | London (Hybrid)
Role Overview
We are seeking an experienced MLOps Engineer to own the infrastructure and operational lifecycle of machine learning systems powering a large-scale clinical monitoring platform. Your work will enable predictive models to identify early signs of clinical deterioration.
Key to these responsibilities is ensuring models are reliably trained, versioned, deployed, and monitored across both cloud and edge environments. As part of a high-ownership role in a fast-paced environment, you'll help foster ML engineering practices including reproducibility, experiment tracking, CI/CD for models, and operational observability.
This is a pivotal role, where your efforts will directly contribute to improving patient outcomes through reliable, scalable and high-performance machine learning systems.
Key Responsibilities
ML Pipeline Orchestration & Automation
- Own and extend ML pipeline orchestration workflows using Apache Airflow, encompassing training, evaluation, and deployment workflows.
- Design and maintain automated pipelines for model retraining, validation, and promotion across environments (development, staging, production).
- Implement pipeline monitoring, alerting, and failure recovery mechanisms ensuring operational reliability.
- Design pipeline architectures supporting rapid experimentation while maintaining reproducibility.
Model Deployment & Serving
- Deploy and manage ML models on AWS, serving batch and production inference workloads.
- Collaborate with firmware and embedded engineering teams to support edge device deployments.
- Administer model versioning, promotion, and rollback workflows using MLflow or comparable tooling.
- Implement strategies for safe model rollouts including shadow deployments and canary releases.
Experiment Tracking & Model Registry
- Maintain and enhance experiment tracking and model registry infrastructure.
- Establish best practices for:
- Experiment logging and artifact storage
- Metadata management and lineage tracking
- Workflows from experimentation to production deployment
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Data & Model Versioning
- Implement and maintain systems for data versioning and dataset management.
- Track dataset lineage, labeling provenance, and feature dependencies.
- Collaborate with ML and data engineering teams to formalise dataset release and validation workflows.
Monitoring, Observability & Data Quality
- Develop model performance monitoring for production, including:
- Drift detection
- Prediction quality tracking
- Construct operational dashboards covering:
- Pipeline health
- Compute utilisation
- Deployment status
- Improve data quality and pipeline reliability with data engineering teams.
- Design incident response procedures and operational runbooks for ML system failures.
Infrastructure & Cost Optimisation
- Manage and optimise AWS compute resources for training and inference workloads.
- Implement Infrastructure-as-Code solutions for reproducible ML environments.
- Execute cost optimisation initiatives across ML compute, storage, and data transfer.
- Support integrations with cloud data warehouses for features and training pipelines.
Elevating ML Practice
- Champion ML engineering best practices, including:
- CI/CD for models
- Automated testing
- Reproducible training workflows
- Build internal tools and templates to accelerate the ML development lifecycle.
- Document operational processes, architectural decisions, and onboarding materials.
- Contribute to architecture discussions and technical planning for scalability
Security & Compliance
- Ensure ML pipelines and infrastructure meet healthcare security and privacy requirements.
- Apply best practices for handling sensitive healthcare data across:
- Training
- Deployment
- Inference processes
- Maintain audit trails covering:
- Model decisions
- Data access
- Deployment history
Required Qualifications
- 4+ years of experience in MLOps, ML Engineering, DevOps, or related infrastructure roles.
- Strong proficiency in Python for ML pipeline development, tooling, and automation.
- Hands-on experience with:
- ML pipeline orchestration tools (particularly Apache Airflow)
- Model registries and experiment tracking platforms (e.g. MLflow)
- Proven experience in:
- Deploying and operating ML workloads on AWS
- Understanding the entire ML lifecycle (training, evaluation, deployment, monitoring, retraining)
- Experience with containerisation technologies (e.g. Docker)
- Infrastructure-as-Code experience
- Proficient in Git and version control workflows.
- Familiarity with SQL and modern data warehousing platforms.
- Monitoring, logging, and alerting experience for production systems.
- Strong debugging and incident response skills for distributed systems.
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Preferred Qualifications
- Experience deploying models to edge or embedded devices
- Background in healthcare, medical devices, or clinical data systems
- Familiarity with model serving frameworks like TorchServe, TensorFlow Serving, or Triton
- CI/CD system experience (e.g. GitHub Actions, Jenkins)
- Data versioning tools (e.g. DVC, LakeFS)
- Supportive experience with data science or ML research teams in production
- Exposure to healthcare compliance and security best practices
- Distributed compute frameworks experience (e.g. Apache Spark, Dask)
- Streaming or real-time inference architectures experience.
What You Bring
- Strong ownership mindset for the entire ML infrastructure lifecycle.
- A relentless focus on reliability, reproducibility, and operational excellence.
- Pragmatic thinking, and lean approach to building scalable ML platforms.
- Comfort in fast-paced, high-growth environments.
- Clear communication skills across engineering, data science, and clinical teams.
- Drive towards making an impact on patient care through technology.
Why Join Us
- Work on real-world healthcare challenges; your contributions have measurable patient impact.
- Build clinical-grade AI and ML applications driving robust data systems.
- Take ownership within a fast-growing, mission-driven environment.
- Collaborate with a highly skilled, multidisciplinary team.
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Skills
MLOps
Python
Airflow
AWS
MLFlow
Docker
Kubernetes
CI/CD
Monitoring
Data Versioning
Model Deployment
Experiment Tracking
Incident Response
Healthcare
Reproducibility
Observability
Location