Overview

Job Title: MLOps Engineer

Qualifications

• Client is seeking a talented and passionate MLOpsEngineer 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 MLOpspipelines 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.