Overview

Job Title: MLOps Engineer

Responsibilities

• VARITE India Private Limited
• Interested candidates are encouraged to respond/submit their updated resumes, and for additional job opportunities, please visit Jobs In India VARITE.
• Refer Candidates and Earn.
• 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.

Qualifications

• 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.
• 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.