Lead Machine Learning Data Engineer T500-19740

Martens is an iconic British brand founded in 1960 in Northamptonshire. Produced originally for workers looking for tough, durable boots, the brand was quickly adopted by diverse youth subcultures and associated musical movements. Martens has since transcended its working-class roots while still celebrating its proud heritage and, six decades later, "Docs" or "DM's" are worn by people around the world who use them as a symbol of empowerment and their own individual attitude. The Company listed on the main market of the London Stock Exchange on 29 January 2021 (DOCS.Reports To: Head of Data Science

Hours: Full-time

Contract: Permanent

As our rockstar ML and Data Science Engineer, you'll be the maestro orchestrating the seamless flow of brilliant machine learning models from the lab into the real world Get ready to team up with the brainy data scientists, the wizardly data engineers, and the cool-headed IT ops crew to make model deployment lightning fast, keep a hawk-eye on their performance, and fine-tune them until they sing. Design, build, and maintain scalable data pipelines to support machine learning models

Model Deployment: Develop and implement automated workflows for seamless production integration of machine learning models using CI/CD practices.

Infrastructure Management: Design and maintain scalable infrastructure for ML workloads, utilizing cloud services (AWS, GCP, Azure) and container orchestration (e.g., Kubernetes).

Monitoring & Performance Optimization: Establish monitoring frameworks to assess model performance, retraining needs, and data drift, ensuring models deliver optimal business value.

Data Management: Ensure proper data pipeline processes, including data cleaning, transformation, and management, is in place to support model training and inference.

Implement best practices related to model security, data privacy, and compliance with applicable regulations.

Required Experience and Skills – Our Must Haves:

~ Bachelor's degree in Computer science, Engineering, Data Science, or a related field. Master's degree preferred.

~4+ years of experience in MLOps, machine learning, or data engineering.

~ Proficiency with ML frameworks (e.g., Strong experience with CI/CD tools (e.g., Jenkins, GitLab CI, CircleCI) to automate deployment processes.

~ Adept in programming languages such as Python , Java, or Scala , with solid knowledge of SQL and NoSQL databases.

~ Familiarity with cloud services (AWS, Azure, Google Cloud) and containerization technologies (Docker, Kubernetes).

~ Experience with data pipeline frameworks (e.g., Strong communication skills to work with cross-functional teams and present findings effectively to non-technical stakeholders.

~ Comfortable with Snowflake centric MLOps leveraging Snowpark developer framework, Snowflake UDFs/store procedures and other SF capabilities for feature engineering and version control.

~ Having experience on any of these MLOps platform Azure ML/ AWS Sagemaker/ MLFLow(Databricks)

Azure ML integration with Snowflake: Use Snowflake as the data layer, Azure ML for model training, and push back results for inference.

Building modular components that can run and scale via Snowpark.

Model Monitoring: Statistical drift detection using Snowflake + Power BI dashboards.

Data Build tool (DBT): Modular, testable, and documented transformations feeding ML workflows.

Experience in designing and implementing MLOps best practices and frameworks.

Drive innovation through creative problem-solving.

Build scalable, future-proof ML infrastructure.

Great relationship management that delivers results through effective teamwork

You'll help build a highly engaged team – ensuring a collaborative culture and providing guidance & support to other team members

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