Senior MLOps Engineer

5 days ago

Bengaluru, Karnataka, India Weekday AI Full time ₹ 15,00,000 - ₹ 20,00,000 per year
Job Description:

Requirements:About the RoleKey Responsibilities
  • End-to-End MLOps Lifecycle: Design, build, and manage machine learning pipelines for data preprocessing, model training, evaluation, deployment, and monitoring in production environments.
  • Automation & CI/CD: Implement CI/CD pipelines for ML models to automate testing, versioning, and deployment workflows.
  • Data Engineering: Work with ETL and big data tools to build robust data ingestion and transformation pipelines that support ML use cases.
  • Model Monitoring: Establish performance tracking, model drift detection, and alerting mechanisms for deployed ML models to ensure continued reliability.
  • Infrastructure Management: Provision and manage cloud-based ML infrastructure using tools like AWS SageMaker, Lambda, ECS, S3, RDS, and EMR.
  • Collaboration: Partner with data scientists and software engineers to streamline the ML lifecycle, reduce friction in deployment, and improve iteration speed.
  • Governance & Compliance: Implement best practices in model governance, reproducibility, and auditability across different stages of the ML workflow.
  • Scalability & Optimization: Ensure the ML infrastructure is scalable, cost-efficient, and optimized for high availability and performance.
Required Skills & Experience
  • 9 years of experience in a combination of machine learning engineering, DevOps, or data engineering roles.
  • Solid expertise in MLOps tools (MLflow, Kubeflow, Airflow, SageMaker Pipelines, etc.).
  • Strong understanding of machine learning workflows, model lifecycle management, and production deployment.
  • Proficiency with ETL frameworks, data processing pipelines, and big data tools (e.g., Spark, Kafka, Hadoop).
  • Hands-on experience with cloud services - particularly AWS (EC2, S3, Lambda, SageMaker, EKS, EMR).
  • Proficient in scripting and programming languages like Python, Bash, and SQL.
  • Experience with containerization and orchestration tools like Docker and Kubernetes.
  • Strong problem-solving skills and ability to debug production ML systems.
Preferred Qualifications
  • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
  • Certifications in AWS or MLOps platforms are a plus.
  • Exposure to security, compliance, and regulatory practices in data and ML workflows.

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