Overview

Job Title: Data Engineer II Location: Gurgaon, HR Experience Required: 3+ years Role Type: Contract-5 months Shift: general Work Mode: Hybrid CTC-18 LPA

Qualifications

• Role Type: Contract-5 months
• Shift: general
• Work Mode: Hybrid
• Python
• PySpark / Spark
• AWS – S3, SageMaker, Lambda, EKS, EMR, Glue, Athena
• MLOps / ML Model Deployment & Inference
• Docker & Kubernetes
• Terraform / IaC
• CI/CD
• Client is seeking a talented and passionate MLOps Engineer to join our growing team.
• Responsible for building Python- and Spark-based ML solutions that ensure the reliability, scalability, and efficiency of machine learning workloads in production.
• Collaborate closely with data scientists, data engineers, and software engineers to operationalize ML models and optimize MLOps workflows.
• Leverage expertise in Python, Spark, ML model inferencing, AWS, and cloud-native technologies to drive data-driven initiatives.
• Design, implement, and maintain scalable and reliable MLOps pipelines on AWS.
• Automate ML model inferencing, deployment, monitoring, retraining, and maintenance workflows.
• Build and maintain Infrastructure as Code (IaC) using Terraform or equivalent tools.
• Implement and manage CI/CD pipelines for API, ML model, and infrastructure deployments.
• Establish automated testing, validation, versioning, and release processes for ML pipelines.
• Implement model and pipeline monitoring, logging, alerting, and operational dashboards.
• Develop reusable automation frameworks and deployment patterns for machine learning workloads.
• Deploy pre-trained machine learning models on AWS using services such as SageMaker, EKS, and AWS Batch.
• Optimize model inference workloads for performance, scalability, availability, and cost efficiency.
• Implement batch and real-time model inference solutions based on business requirements.
• Manage containerized ML workloads using Docker and Kubernetes.
• Support model versioning, rollback, blue/green deployments, and controlled production releases.
• Troubleshoot model serving, deployment, networking, and infrastructure-related issues.
• Design and implement data pipelines for ML inference using AWS services such as S3, EMR, Glue, and Athena.
• Develop scalable data processing workflows using Apache Spark and Python.
• Ensure data quality, consistency, validation, and availability for ML pipelines.