Machine Learning Engineer - 5

Adobe Adobe · Enterprise · Bangalore, India

Machine Learning Engineer focused on MLOps/Platform engineering for Adobe's Advertising Cloud Search, Social, Commerce platform. Responsibilities include managing the model lifecycle, developing CI/CD and orchestration workflows, monitoring production models, and ensuring governance, security, and compliance for ML pipelines on AWS.

What you'd actually do

  1. Manage model versioning, deployment strategies, rollback mechanisms, and A/B testing frameworks.
  2. Develop CI/CD and orchestration workflows using GitLab CI, GitHub Actions, CircleCI, Airflow, Argo Workflows, or similar tools.
  3. Review and optimize data science models, including code refactoring, containerization, deployment, versioning, and performance tuning.
  4. Implement model testing, validation, and automated QA pipelines, ensuring reproducibility and compliance.
  5. Monitor models in production, including data drift, concept drift, performance degradation, and system reliability.

Skills

Required

  • Strong ability to design and implement cloud architectures for end-to-end ML workflows on AWS.
  • Hands-on experience with MLOps frameworks like MLflow, Kubeflow, Airflow or similar.
  • Proficiency with Docker, Kubernetes (EKS/GKE/AKS), and enterprise platforms like OpenShift.
  • Strong programming skills in Python; familiarity with Go, Ruby, or Bash scripting.
  • Experience with common ML libraries such as scikit-learn, TensorFlow, Keras, PyTorch.
  • Experience with software engineering guidelines including version control, testing, and automation.
  • Ability to understand data science workflows, experiment tracking, and feature engineering tools.
  • Strong communication skills; ability to work collaboratively in multi-functional teams.
  • Knowledge of cloud services such as AWS Sagemaker, Azure ML, GCP Vertex AI.
  • Experience with observability tools (Prometheus, Grafana, ELK, CloudWatch, Datadog).
  • Experience implementing model governance & lineage with tools like MLflow Registry, SageMaker Model Registry, or Vertex ML Metadata.
  • Familiarity with infrastructure-as-code (Terraform, CloudFormation).

Nice to have

  • Exposure to feature stores like Feast, Tecton, or Databricks Feature Store.

What the JD emphasized

  • ML Ops/ Platform engineering
  • end-to-end ML workflows on AWS
  • MLOps frameworks
  • model governance & lineage

Other signals

  • ML Ops/ Platform engineering
  • Manage model versioning, deployment strategies, rollback mechanisms, and A/B testing frameworks
  • Develop CI/CD and orchestration workflows
  • Monitor models in production
  • Ensure governance, security, and compliance for ML pipelines