MLOps Engineer / Machine Learning Data Engineer

We are

At Synechron, we believe in the power of digital to transform businesses for the better. Our global consulting firm combines creativity and innovative technology to deliver industry-leading digital solutions. Synechron’s progressive technologies and optimization strategies span end-to-end Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. With top clients to boast about, Synechron has a global workforce of 14,500+, and has 58 offices in 21 countries within key global markets.

Key Responsibilities:

• Design, develop, and maintain robust ETL pipelines using AWS services such as Glue, Lambda, Step Functions, S3, Athena, Redshift, and EMR.
• Leverage DHP for secure, scalable, and compliant data hosting and management.
• Collaborate with data scientists to operationalize ML models and integrate them into production data pipelines.
• Solid understanding of ML lifecycle, including data preparation, model training, deployment, and monitoring.
• Familiarity with ML model deployment and integration (e.g., using SageMaker, MLflow, or custom APIs)
• Implement and maintain CI/CD pipelines for automated deployment and testing of data workflows.
• Experience with infrastructure-as-code tools (e.g., Terraform or CloudFormation).
• Optimize data workflows for performance, scalability, and cost-efficiency in the cloud.
• Implement data quality checks, monitoring, and alerting mechanisms.
• Work with structured and unstructured data from various sources including APIs, databases, and streaming platforms.
• Ensure compliance with data governance, privacy, and security standards.
• Participate in code reviews, architecture discussions, and agile ceremonies.

Preferred Qualifications:

• AWS Certification (e.g., AWS Certified Data Analytics – Specialty, Machine Learning – Specialty).
• Experience with Apache Spark, Airflow, or other orchestration tools.
• Exposure to MLOps practices and tools for scalable ML operations.
• Familiarity with data cataloging, metadata management, and data lineage tools.