Job title: Lead Machine Learning Data Engineer
Company: Dr. Martens
Job description: Job DescriptionAs 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! You’ll be the architect, the builder, and the guardian of the pipelines that make magic happen. 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. Its a global adventure and would entail collaboration with global teams across time zones to ensure operational continuity.THE GIG
- Development: 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.
- Security & Compliance: Implement best practices related to model security, data privacy, and compliance with applicable regulations.
- Documentation and best practices: Produces architecture and design artifacts for complex applications, accountable for design constraints and best practices for code development.
THE STUFF THAT SETS YOU APARTRequired Experience and Skills Our Must Haves
- Bachelors degree in computer science, Engineering, Data Science, or a related field. Masters degree preferred.
- 8+ years of experience in MLOps, machine learning, or data engineering.
- Proficiency with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and understanding of supervised and unsupervised learning algorithms.
- 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., Apache Airflow, Apache Kafka).
- Excellent problem-solving and analytical skills, with the ability to troubleshoot complex issues.
- 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)
Nice to have:
- Azure ML integration with Snowflake: Use Snowflake as the data layer, Azure ML for model training, and push back results for inference.
- Anomaly Detection & AutoML: 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.
Core Competencies
- Strong problem-solving skills with a proactive approach to optimisation.
- Excellent communication skills and ability to collaborate across global teams and time zones.
- Experience in designing and implementing MLOps best practices and frameworks.
Expected salary:
Location: Bangalore, Karnataka
Job date: Wed, 14 May 2025 07:35:16 GMT
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