MLOps Engineer

2 days ago

Bengaluru, Karnataka, India SWITS DIGITAL Private Limited Full time ₹ 12,00,000 - ₹ 24,00,000 per year

Job Title:
MLOps Engineer

Experience:
7 – 12 Years

Location:
Bangalore

Job Summary
We are seeking an experienced
MLOps Engineer
with strong expertise in
Data Science
and
AWS Cloud
to design, deploy, and maintain end-to-end machine learning solutions. The ideal candidate will have hands-on experience in building scalable MLOps pipelines, automating model lifecycle processes, and optimizing ML infrastructure in production environments. This role offers the opportunity to work with cutting-edge technologies, collaborating closely with Data Scientists and Engineers to drive innovation and efficiency in AI-driven projects.

Key Responsibilities

  • Design, develop, and maintain scalable MLOps pipelines for automated model training, testing, and deployment.
  • Collaborate with Data Scientists to productionize machine learning models and integrate them into business systems.
  • Automate data preprocessing, model training, and deployment workflows using modern orchestration tools.
  • Implement and manage CI/CD pipelines for ML models using Jenkins, GitHub Actions, or AWS CodePipeline.
  • Manage and optimize ML infrastructure on AWS (SageMaker, Lambda, ECS/EKS, EC2, ECR, S3, CloudWatch).
  • Monitor model performance, implement retraining pipelines, and ensure reliability and scalability of deployed models.
  • Ensure best practices in version control, reproducibility, and governance for ML assets.
  • Collaborate with cross-functional teams (Data Engineering, DevOps, and Product) to enhance ML workflows and delivery efficiency.
  • Conduct performance optimization and cost management for ML infrastructure.
  • Stay updated on emerging technologies in MLOps and AI to continuously improve operational processes.

Required Skills

  • Strong programming experience in Python (Pandas, NumPy, Scikit-learn, etc.).
  • Hands-on experience with AWS Cloud Services – SageMaker, Lambda, ECS/EKS, EC2, ECR, S3, and CloudWatch.
  • Proficiency in MLOps frameworks such as MLflow, Kubeflow, Airflow, or similar.
  • Strong understanding of machine learning model lifecycle, deployment strategies, and monitoring techniques.
  • Experience with Docker and Kubernetes for containerized deployments.
  • Practical knowledge of CI/CD pipelines and automation tools (Jenkins, GitHub Actions, AWS CodePipeline).
  • Familiarity with data pipelines, feature stores, and Git-based version control systems.
  • Strong problem-solving, debugging, and performance optimization skills.

Good To Have

  • Experience with Terraform or CloudFormation for infrastructure as code (IaC).
  • Exposure to DataOps or Feature Store design.
  • Knowledge of model governance, compliance, and auditability in ML workflows.
  • Familiarity with PyTorch, TensorFlow, or Hugging Face model deployment.
  • Experience in monitoring tools (Prometheus, Grafana, or ELK Stack).

Soft Skills

  • Strong analytical and communication skills.
  • Ability to work collaboratively across teams in a fast-paced environment.
  • Passion for automation, scalability, and production-grade AI solutions.
  • Continuous learning mindset to stay updated with the latest in ML and DevOps.

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