Job Description

Our Company



Changing the world through digital experiences is what Adobe's all about. We give everyone--from emerging artists to global brands--everything they need to design and deliver exceptional digital experiences! We're passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen.



We're on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours!



The Challenge



We are looking for a highly motivated individuals knowledgeable in ML Ops/ Platform engineering to be part of our Advertising Cloud Search, Social, Commerce, a leading digital marketing spend optimization platform helping enterprise clients achieving their marketing goals. We are now faced with exciting challenge of continuing the legacy and at the same time evolving and owning the digital marketing space through our innovative solutions. It's a robust agile team that collaborates and works across all functions of true product development. You must be that individual who is highly engaged, motivated and excels at critical thinking and problem solving!



What you'll do

Model Lifecycle Management :

Manage model versioning, deployment strategies, rollback mechanisms, and A/B testing frameworks . Coordinate model registries, artifacts, and promotion workflows in collaboration with ML Engineers Develop CI/CD and orchestration workflows using GitLab CI, GitHub Actions, CircleCI, Airflow, Argo Workflows, or similar tools.

Review and optimize data science models, including code refactoring, containerization, deployment, versioning, and performance tuning. Implement model testing, validation, and automated QA pipelines, ensuring reproducibility and compliance.

Monitor models in production, including data drift, concept drift, performance degradation, and system reliability.

Collaborate multi-functionally with data scientists, data engineers, and architects; build documentation and improve team processes.

Ensure governance, security, and compliance for ML pipelines (access controls, audit logs, model reproducibility, lineage).

What you need to succeed

Bachelor's degree or advanced degree or equivalent experience in Computer Science, Software Engineering or a related technical field.

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.

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

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).



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