Responsibilities
- Design, develop, and deploy machine learning models using AWS SageMaker platform.
- Build and maintain ML pipelines for training, validation, and deployment of models.
- Implement MLOps best practices including CI/CD for machine learning workflows.
- Collaborate with data scientists to productionize research models.
- Monitor model performance and implement automated retraining processes.
- Optimize model inference performance and cost efficiency.
- Develop and maintain model versioning and experiment tracking systems.
- Ensure data quality and implement data validation frameworks.
- Create comprehensive documentation and technical specifications.
- Participate in code reviews and maintain high coding standards.
- Debug Terraform and Concourse errors.
- Proactively update pipelines based on changes made by other organizations.
- Migrate repository to GitHub and update pipelines accordingly.
Basic qualifications
- Bachelor's degree in Computer Science, Data Science, Engineering, or related field; or 8 years of equivalent work experience.
- 3+ years of experience in machine learning engineering or related roles.
- Proficiency in Python programming with experience in ML libraries (pandas, numpy, etc.).
- Familiarity with Infrastructure as Code (Terraform, CloudFormation).
- Hands-on experience with AWS SageMaker for model training, tuning, and deployment.
- Strong background in data science methodologies and statistical analysis.
- Deep understanding of MLOps practices and tools (Docker, Kubernetes, CI/CD pipelines).
- Experience with version control systems (Git Hub Actions) and collaborative development.
- Knowledge of cloud platforms, preferably AWS (S3, EC2, Lambda, etc.).
Preferred qualifications
- Master's degree in a relevant field.
- AWS certifications (Machine Learning Specialty, Solutions Architect, etc.).
- Knowledge of containerization and orchestration technologies.
- Experience with monitoring and observability tools (CloudWatch, Prometheus, etc.).
- Experience with big data technologies (EMR, Spark, Hadoop, etc.).
- Understanding of software engineering best practices and design patterns.
- Good working experience in ETL (SSIS or Sqoop/Spark).
- Experience with EMR
- Expert SQL knowledge (All types of Joins, CTE’s, Indexes, Stored Procedures, SQL performance).
- Knowledge in building basic machine learning models (Classification & Regression).
- Knowledge in Docker/MLOps and its orchestrations.
- Strong analytical and problem-solving abilities.
- Excellent communication and collaboration skills.
- Ability to work in fast-paced, agile environments.
- Detail-oriented with a focus on code quality and documentation.
- Continuous learning mindset and adaptability to new technologies.
- Experience working cross-functionally with data scientists, engineers, and product teams.
Tags & focus areas
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