ML Ops Engineer: Production ML Pipelines & Edge Deployments

Circadia Health; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Circadia Health is seeking an experienced ML Ops Engineer to own the infrastructure and lifecycle of our production ML systems. You will build and maintain end-to-end ML pipelines, deployment infrastructure, and monitoring to keep predictive models accurate and reliable across cloud and edge environments.

You will collaborate with ML, data, and clinical teams to ensure reproducibility, CI/CD for models, and robust observability while maintaining healthcare-grade security and privacy standards.

Qualifications

• 4+ years of experience in MLOps, ML engineering, DevOps or similar infrastructure roles.
• Strong proficiency in Python for ML pipeline development, tooling, and automation.
• Hands-on experience with ML pipeline orchestration tools (Airflow).
• Experience with model registries and experiment tracking platforms (MLflow preferred).
• Experience deploying ML workloads on AWS (Batch, EC2, S3, IAM, CloudWatch).
• Familiarity with CI/CD for ML and containerisation (Docker).
• Experience with Snowflake or data warehousing environments.

Responsibilities

• Own and extend Circadia’s ML pipeline orchestration using Apache Airflow for training, evaluation, and deployment workflows.
• Build and maintain automated pipelines for model retraining, validation, and promotion across development, staging, and production.
• Implement pipeline monitoring, alerting, and failure recovery to ensure operational reliability.
• Design architectures that support rapid experimentation while ensuring production-grade reproducibility.
• Deploy and manage ML models on AWS infrastructure (e.g., AWS Batch for batch inference).
• Support deployment of models to edge devices, coordinating with firmware and embedded teams.
• Manage model versioning, promotion, and rollback through MLflow registry.
• Establish and enforce logging, artifact storage, and lineage tracking for experiments.

Skills

Python

Airflow

MLflow

AWS

Docker

Git

SQL

Snowflake

CI/CD

Tools

AWS Batch

EC2

CloudWatch

S3

Terraform

Kubernetes

Job description

Circadia Health is seeking an experienced ML Ops Engineer to own the infrastructure and lifecycle of our production ML systems. You will build and maintain end-to-end ML pipelines, deployment infrastructure, and monitoring to keep predictive models accurate and reliable across cloud and edge environments.

You will collaborate with ML, data, and clinical teams to ensure reproducibility, CI/CD for models, and robust observability while maintaining healthcare-grade security and privacy standards.