Job Description
Job Summary (List Format):
Technical Responsibilities:
- Architect and support infrastructure solutions for a Data Analytics platform on AWS.
- Hands-on experience with AWS compute services:
- EKS (Kubernetes solutioning and operations)
- Lambda with AWS EventBridge
- Proficient in AWS Database and Storage services:
- S3 (medallion architecture), FSx (Lustre, ONTAP, Windows), RDS
- Experience with AWS Data & Reporting services:
- Airflow (MWAA), AWS Glue (catalog, crawlers), Redshift, Tableau
- Manage Data Migration services:
- S3 replication across accounts (Control Tower)
- Data synchronization from on-premises to S3
- AWS Transfer Family
- Knowledge of AWS foundational services: VPC, ASG, EC2, Secrets Manager, KMS
- Strong programming skills in Python and/or R
- Experience with data validation pipelines, SQL, and Spark
- Solid understanding of CI/CD principles; experience with tools like GitHub Actions
- Design and build scalable MLOps pipelines on AWS (automation of model training, evaluation, deployment, monitoring)
- Manage version control for models, data, and configurations
Secondary/Supporting Responsibilities:
- Automate ML model retraining and rollback processes
- Document ML systems, workflows, and infrastructure
- Support rapid prototyping and deployment of ML solutions for research and experimentation
- Collaborate with data scientists, software engineers, and DevOps teams to integrate ML models into applications
Communication & Interpersonal Skills:
- Communicate effectively with internal and customer stakeholders via verbal, email, and instant messaging
- Strong interpersonal skills for building relationships and providing/receiving constructive feedback in code reviews
Problem-Solving & Work Management:
- Strong troubleshooting and analytical skills
- Experience working in Agile/Scrum environments; familiarity with Jira
- Provide regular, proactive work updates and demonstrate due diligence in responsibilities
Job Tags
Full time, Part time, Seasonal work,
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