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Job Description & How to Apply Below
Data Engineer II
Location:
Gurgaon, HR
Experience
Required:
3
Role Type/Shift : Contract/NA
About The Job:
Company Name: VARITE India Private Limited
About The Client:
An American technological research and consulting firm based in Stamford, Connecticut that conducts research on technology and shares this research through private consulting, executive programs, and conferences. Its clients include large corporations, government agencies, technology companies, and investment firms.
The Client serves over 12,000 organizations in over 100 countries with an employee strength of 15,000.
About The Job:
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.
Essential Job Functions:
MLOps Infrastructure & Automation:
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.
Deployment & Scaling:
Deploy pre-trained machine learning models on AWS using services such as Sage Maker, 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.
Data Engineering & Management:
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.
Optimize data storage, processing, and retrieval to improve ML inference performance.
Work with structured and unstructured datasets and support large-scale data processing.
Implement data lineage, pipeline monitoring, and error-handling mechanisms.
Collaboration & Communication:
Collaborate with data scientists and fellow engineers to ensure smooth model deployment and production operations.
Partner with data engineering, Dev Ops, and cloud teams to establish scalable ML infrastructure.
Communicate technical concepts, implementation approaches, and findings effectively to technical and non-technical stakeholders.
Participate in code reviews and contribute to engineering standards and MLOps best practices.
Troubleshoot and resolve production issues related to ML pipelines, model inference, applications, and infrastructure .
Document architecture, deployment procedures, operational processes, and troubleshooting guidelines.
Continuously evaluate new MLOps technologies and recommend improvements to existing platforms and workflows.
Qualifications:
Data Engineer:
Strong proficiency in Python and Spark.
Experience with AWS cloud services particularly Sagemaker, Lambda, EKS, S3, Glue, EMR and Terraform or other equivalent cloud (IAC).
Experience with containerization technologies (e.g., Docker, Kubernetes).
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field with 3–5 years of experience working with data and ML services using Python and Spark.
Strong proficiency in Python and Apache Spark .
Hands-on experience with AWS cloud services, particularly Sage Maker, Lambda, EKS, S3, Glue, EMR, and Athena .
Strong experience with Terraform or equivalent Infrastructure as Code (IaC) technologies.
Experience with Docker and Kubernetes and containerized application deployment.
Experience…
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