Lead Machine Learning Data Engineer T500-19792

1 week ago

Bengaluru, Karnataka, India ANSR Full time
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.Required Experience and Skills:

6- 10 years of experience in MLOps, ML Model Development and data engineering / ML engineering.

~ Candidates should have developed and deployed the ML Models End to END.

~ MLOps Or Microsoft Azure Machine Learning Or AWS Sage maker OrMLFlow

Python or Scala

Docker or Kubernetes

Some experience in GenAI frameworks (Ability to contextualize and fine tune Foundation models)

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

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 Sage maker / 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.

We encourage applications, regardless of race, colour, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, age, veteran status, or disability. Diverse and inclusive teams have a positive impact on our brand helping us to speak authentically to our customers.

Martens community.

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