Machine Learning Engineer 4

What You’ll Do:   Develop classifiers, predictive models and multi variate optimization algorithms on large-scale datasets using advanced statistical modeling, 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&D will revolve around a few key focus areas such as Agentic AI solutions, predictive models for conversion optimization, Reinforcement Learning, and Forecasting & Planning.  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 require:  7-9 yrs. of relevant experience as 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 optimization techniques, GenAI/LLM/Agentic  solutions.   Hands-on experience with MLOps frameworks like MLflow, Kubeflow, Airflow or similar.  Experience with control systems, reinforcement learning problems, contextual bandit  algos  Experience with common ML libraries such as scikit-learn, TensorFlow, Keras, 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, 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.  Strong communication skills; ability to work collaboratively in multi-functional teams & articulate the design and implementation choices to cross functional teams.  General understanding of data structures, algorithms, multi-threaded programming and distributed computing concepts  Ability to be a self-starter and work closely with other data scientists and software engineers to design, test and build production ready ML and optimization models and distributed algorithms running on large scale data sets  Strong analytical, quantitative problem solving, and communication skills  Proven ability to work well in a high performing team with agile development approaches and technolog About Adobe Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. 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