Machine Learning Engineer

The Adobe Advertising ML team is looking for an experienced Machine Learning Engineer to join our AI/ML engineering org. You will join a growing team of data scientists and engineers to help us build and maintain the next generation of predictive models, optimisation algorithms and Agentic AI solutions. You will develop production-ready features for our worldwide clients in a fast-paced environment.
Responsibilities:
Develop classifiers, predictive models and multivariate optimisation algorithms onlarge-scale datasets using advanced statistical modelling, machine learning and data mining.
Design, implement and operate scalable models that can work with large-scale datasets(100s billions of records) in production systems.
Ability to articulate the design and implementation choices to cross-functional teams.
R and D will revolve around a few key focus areas, such as Agentic AI solutions, predictive models for conversion optimisation, Reinforcement Learning, and Forecasting and Planning.
Model Lifecycle Management: Manage model versioning, deployment strategies, rollback mechanisms, and A/B testing frameworks.
Coordinate model registries, artefacts, and promotion workflows in collaboration with ML Engineers.
Develop CI/CD and, orchestration workflows using GitLab CI, GitHub Actions, CircleCI, Airflow, ArgoWorkflows, or similar tools.
Review and optimise 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, performancedegradation, and system reliability.
Collaborate multi-functionally with data scientists, data engineers, and architects; builddocumentation and improve team processes.
Ensure governance, security, and compliance for ML pipelines (access controls, audit logs, model reproducibility, lineage).
Requirements:
4-6 years of relevant experience as an ML engineer.
Strong programming skills in Python, Java/Scala, SQL, Hive, Spark.
Experience working on production systems involving machine learning, NLP, classifiers, statistical modeling and multivariate optimisation techniques, GenAI/LLM/Agentic solutions.
Hands-on experience with MLOps frameworks like MLflow, Kubeflow, Airflow or similar.
Experience with control systems, reinforcement learning problems, and contextual bandit algos.
Experience with common ML libraries such as scikit-learn, TensorFlow, Keras, and PyTorch.
Experience with software engineering guidelines, including version control, testing, and automation.
Experience with observability tools (Prometheus, Grafana, ELK, CloudWatch, Datadog).
Knowledge of cloud services such as AWS Sagemaker, Azure ML, and GCP Vertex AI.
Knowledge of Docker, Kubernetes (EKS/GKE/AKS), and enterprise platforms like OpenShift.
Familiarity with infrastructure-as-code (Terraform, CloudFormation).
Strong ability to design and implement cloud architectures for end-to-end ML workflows on AWS.
Ability to understand data science workflows, experiment tracking, and feature engineering tools.

Experience
4-7 yrs