Data/MLOps Engineer – CT&C

Upvanta is hiring for their CT&C Engineering team, focusing on bridging Data Science and Production Engineering to ensure scalable, reliable, secure, and production-ready machine learning solutions.

• Strong Python development experience.
• Hands-on experience with Apache Spark and PySpark.
• Solid understanding of machine learning lifecycle management and MLOps best practices.
• Experience with AWS services, particularly Amazon SageMaker, AWS Lambda, and AWS CDK.
• Experience building CI/CD pipelines for data and ML workloads.
• Strong SQL skills.
• Experience designing and implementing ETL/ELT pipelines.
• Knowledge of PyTorch and machine learning frameworks.
• Experience with Infrastructure as Code (Terraform and/or CloudFormation).
• Understanding of monitoring, observability, and production support practices.
• Experience working in Agile environments.
• Experience with Feature Stores and Model Registry platforms.
• Experience implementing Continuous Training (CT) pipelines.
• Knowledge of MLOps governance frameworks.
• Experience with real-time streaming architectures.
• Exposure to large-scale cloud-native data platforms.

ML & Data Infrastructure:

• Deploy, maintain, and optimize end-to-end machine learning lifecycles, including automated training, deployment, monitoring, and versioning.
• Build and support core MLOps capabilities such as Feature Stores, Experiment Tracking platforms, and Model Registries.
• Provision and manage scalable cloud infrastructure using Infrastructure as Code (IaC) solutions such as Terraform or AWS CloudFormation.
• Design and implement robust CI/CD/CT (Continuous Training) pipelines to enable reliable and repeatable production releases.
• Collaborate closely with Data Scientists to productionize machine learning models and workflows.

Data Engineering & Pipeline Optimization:

• Design and develop high-volume data ingestion and processing pipelines using Apache Spark, PySpark, and Python.
• Build scalable ETL/ELT solutions supporting advanced analytics and machine learning workloads.
• Implement optimized data models and storage strategies to support low-latency model inference and high-performance analytics.
• Integrate automated data quality validation, monitoring, and observability capabilities across data platforms.

Governance, Monitoring & Security:

• Implement proactive monitoring for model performance, model drift, data quality issues, and system latency.
• Ensure complete reproducibility through robust versioning of data, code, models, and artifacts.
• Apply security best practices across the ML lifecycle, including access management, data privacy, and compliance requirements.
• Support operational excellence through incident management, troubleshooting, and continuous improvement initiatives.

Agile Delivery & Collaboration:

• Work within Agile delivery teams, participating in sprint planning, backlog refinement, daily stand-ups, and retrospectives.
• Translate business and data science requirements into scalable technical solutions.
• Collaborate with Product Owners, Data Scientists, Data Engineers, and Platform Teams to deliver production-grade ML solutions.