Machine Learning Operations (MLOps) Engineer

Do you love a career where you Experience, Grow & Contribute at the same time, while earning at least 10% above the market? If so, we are excited to have bumped onto you. Learn how we are redefining the meaning of work, and be a part of the team raved by Clients, Job-seekers and Employees. Jobseeker Video Testimonials Employee Glassdoor Reviews If you are a Machine Learning Operations (MLOps) Engineer looking for excitement, challenge and stability in your work, then you would be glad to come across this page.

We are an IT Solutions Integrator/Consulting Firm helping our clients hire the right professional for an exciting long-term project. Here are a few details. Check if you are up for maximizing your earning/growth potential, leveraging our Disruptive Talent Solution. Role:Machine Learning Operations (MLOps) Engineer Location: Hyderabad | Bengaluru | Chennai | Pune | Mumbai | Kolkata | Gurgaon Work Mode: Hybrid Relevent Experience: 6-9 Years Type: Contract to Hire Requirements Key Responsibilities ML CI/CD & Deployment Design, build, and maintain CI/CD pipelines for Machine Learning workflows, including: Model training Model validation Model packaging Model deployment Ensure ML pipelines operate efficiently across development, testing, and production environments.

Model Deployment & Serving Implement and manage model deployment patterns, including: Batch inference Real-time inference Streaming inference Develop and maintain model serving infrastructure for scalable and reliable ML inference. Model Observability & Monitoring Establish comprehensive model observability frameworks to monitor: Data drift Model performance degradation Latency System failures Bias and quality signals Feature Engineering Infrastructure Build and manage feature pipelines and feature stores.

Ensure data lineage, reproducibility, and traceability across ML workflows. Experiment Management & Model Governance Operationalize experiment tracking frameworks. Manage model registry and artifact management systems, including: Versioning of code Versioning of datasets Versioning of models Model Testing & Validation Define and automate testing frameworks for ML systems, including: Unit testing Integration testing Implement validation gates and model promotion criteria before deployment to production.

Security & Compliance Collaborate with security and compliance teams to implement: Access controls Secrets management Audit logging Risk management controls Performance Optimization Optimize infrastructure for training and inference workloads, including: Autoscaling Resource right-sizing GPU utilization Workload scheduling Ensure efficient compute utilization and cost optimization.

Operational Excellence Develop and maintain: Operational runbooks SLAs (Service Level Agreements) SLOs (Service Level Objectives) Incident response processes Operational monitoring dashboards Architecture & Platform Standards Contribute to reference architectures for machine learning platforms. Develop engineering standards, reusable templates, and best practices for ML product teams.

Required Skills

& Expertise Strong experience in Machine Learning Operations (MLOps) and ML platform engineering Expertise in CI/CD pipelines for ML workflows Experience managing ML model deployment patterns (batch, real-time, streaming) Knowledge of model observability and monitoring Hands-on experience with feature pipelines and feature stores Experience implementing experiment tracking, model registry, and artifact management Familiarity with model testing frameworks (unit and integration testing) Strong understanding of ML governance, security, and compliance practices Experience with autoscaling infrastructure, GPU utilization, and workload scheduling Ability to build operational dashboards and incident management processes Strong experience designing ML reference architectures and reusable engineering templates Key Focus Areas ML CI/CD pipelines Model deployment and serving infrastructure Model monitoring and observability Feature store management Experiment tracking and artifact management Testing automation for ML systems Security, compliance, and governance Cost optimization and GPU utilization Operational reliability (SLA/SLO/Incident management) Benefits Visit us at http://alignity.

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