MLOPs Engineer
Posted Today by Harnham
£640 Per day
Outside
Hybrid
England, United Kingdom
Summary: The MLOps Engineer role is focused on establishing operational excellence within a large data function for a leading global e-commerce client. The position involves scaling the core on-site advertising platform from batch processing to real-time capabilities, requiring a hands-on expert in MLOps. Responsibilities include designing MLOps processes, building real-time pipelines, and mentoring a large engineering team. The role offers flexibility for remote work with a requirement for occasional office presence.
Key Responsibilities:
- Design and deploy end-to-end MLOps processes with a focus on governance, reproducibility, and automation.
- Architect and implement solutions for transitioning high-volume model serving to real-time performance.
- Lead the integration and use of MLflow for model registry, experiment tracking, and deployment within Databricks.
- Build and automate CI/CD pipelines using GIT for stable and frequent model releases.
- Profile and optimise large-scale Spark/Python codebases for production efficiency.
- Act as the technical lead to embed MLOps standards into the core Data Engineering team.
Key Skills:
- Proven experience designing and implementing end-to-end MLOps processes in a production environment.
- Expert proficiency with Databricks and MLflow.
- Expert Apache Spark and Python engineering experience on large datasets.
- Strong experience with GIT for version control and building CI/CD/release pipelines.
- Excellent SQL skills.
- Familiarity with Google Cloud Platform (GCP).
- Good understanding of math/model fundamentals for optimisation.
- Familiarity with low-latency data stores (e.g., CosmosDB).
Salary (Rate): £640.00/daily
City: undetermined
Country: United Kingdom
Working Arrangements: hybrid
IR35 Status: outside IR35
Seniority Level: Senior
Industry: IT
MLOps Engineer Outside IR35 - 500-600 Per Day Ideally, 1 day per week/fortnight in the office, flexibility for remote work for the right candidate. A market-leading global e-commerce client is urgently seeking a Senior MLOps Lead to establish and drive operational excellence within their largest, most established data function (60+ engineers). This is a mission-critical role focused on scaling their core on-site advertising platform from daily batch processing to real-time capability. This role suits a hands-on MLOps expert who is capable of implementing new standards, automating deployment lifecycles, and mentoring a large engineering team on best practices.
What you'll be doing:
- MLOps Strategy & Implementation: Design and deploy end-to-end MLOps processes, focusing heavily on governance, reproducibility, and automation.
- Real-Time Pipeline Build: Architect and implement solutions to transition high-volume model serving (10M+ customers, 1.2M+ product variants) to real-time performance.
- MLflow & Databricks Mastery: Lead the optimal integration and use of MLflow for model registry, experiment tracking, and deployment within the Databricks platform.
- DevOps for ML: Build and automate robust CI/CD pipelines using GIT to ensure stable, reliable, and frequent model releases.
- Performance Engineering: Profile and optimise large-scale Spark/Python codebases for production efficiency, focusing on minimising latency and cost.
- Knowledge Transfer: Act as the technical lead to embed MLOps standards into the core Data Engineering team.
Key Skills:
- Must Have:
- MLOps: Proven experience designing and implementing end-to-end MLOps processes in a production environment.
- Cloud ML Stack: Expert proficiency with Databricks and MLflow.
- Big Data/Coding: Expert Apache Spark and Python engineering experience on large datasets.
- Core Engineering: Strong experience with GIT for version control and building CI/CD / release pipelines.
- Data Fundamentals: Excellent SQL skills.
- Nice-to-Have/Desirable Skills
- DevOps/CICD (Pipeline experience)
- GCP (Familiarity with Google Cloud Platform)
- Data Science (Good understanding of math/model fundamentals for optimisation)
- Familiarity with low-latency data stores (e.g., CosmosDB).
If you have the capability to bring MLOps maturity to a traditional Engineering team using the MLFlow/Databricks/Spark stack, please email: with your CV and contract details.