Data Engineer

VARITE INC; Gurugram District; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

VARITE India Private Limited is seeking an experienced MLOps Engineer to design and implement scalable ML pipelines on AWS, focusing on reliability and performance.

You will collaborate with data scientists and software engineers to operationalize models using Python, Spark, and cloud-native tech, including CI/CD and IaC practices.

Qualifications

• Strong proficiency in Python and Spark with 3–5 years of data/ML experience.
• Hands-on AWS: SageMaker, Lambda, EKS, S3, Glue, EMR, Athena.
• Experience with Terraform or equivalent IaC and containerization (Docker, Kubernetes).
• Experience deploying ML models, serving, and inference workflows.
• Ability to design scalable, production-ready MLOps pipelines and CI/CD for ML workloads.
• Collaborative, cross-functional team player with strong problem-solving skills.

Responsibilities

• Design, implement, and maintain scalable MLOps pipelines on AWS.
• Automate ML model inference, deployment, monitoring, retraining, and maintenance workflows.
• Build IaC using Terraform and manage CI/CD for ML deployments.
• Deploy models on SageMaker, EKS, and AWS Batch; optimize performance.
• Ensure data quality, lineage, and availability in ML pipelines.
• Collaborate with data scientists, data engineers, and DevOps for production operations.

Skills

Python

Spark

AWS

Terraform

Docker

Kubernetes

CI/CD

ML model deployment

ML pipelines

Education

Bachelor's or Master's in Computer Science, Engineering, Data Science or related field

Tools

SageMaker

Lambda

EKS

S3

Glue

EMR

Athena

Terraform (IaC)

Docker

Kubernetes

Job description

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 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.
• 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.
• Collaborate with data scientists and fellow engineers to ensure smooth model deployment and production operations.
• Partner with data engineering, DevOps, 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

• 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 SageMaker, 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 developing and managing CI/CD pipelines for ML applications and infrastructure.
• Strong understanding of ML model deployment, model serving, and inference workflows.
• Exposure to MLOps platforms, tools, and best practices.
• Exposure to Big Data technologies and data engineering concepts.
• Understanding of cloud security, IAM, networking, monitoring, and AWS best practices.
• Experience with REST APIs, serverless architectures, and event-driven workflows is an added advantage.
• Strong problem-solving, troubleshooting, and analytical skills.
• Ability to work effectively in a collaborative, cross-functional team environment.
• Strong understanding of software development lifecycle, version control, testing, and deployment practices.
• Ability to design reliable, scalable, and production-ready ML solutions.

About VARITE

VARITE is a global staffing and IT consulting company providing technical consulting and team augmentation services to Fortune 500 Companies in USA, UK, CANADA and INDIA. VARITE is currently a primary and direct vendor to the leading corporations in the verticals of Networking, Cloud Infrastructure, Hardware and Software, Digital Marketing and Media Solutions, Clinical Diagnostics, Utilities, Gaming and Entertainment, and Financial Services.