Machine Learning Engineer 4

at Adobe · Enterprise · Bangalore, India

Machine Learning Engineer at Adobe responsible for developing, implementing, and operating scalable ML models, with a focus on Agentic AI solutions, predictive modeling, Reinforcement Learning, and Forecasting. The role involves end-to-end model lifecycle management, MLOps, performance tuning, production monitoring, and ensuring governance, security, and compliance for ML pipelines.

What you'd actually do

  1. Develop classifiers, predictive models and multi variate optimization algorithms on large-scale datasets using advanced statistical modeling, machine learning and data mining.
  2. Design, implement and operate scalable models that can work with large-scale datasets (100s billions of records) in production systems.
  3. 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.
  4. 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.
  5. Monitor models in production, including data drift, concept drift, performance degradation, and system reliability.

Skills

Required

  • Python
  • Java/Scala
  • SQL
  • Hive
  • Spark
  • MLOps frameworks like MLflow, Kubeflow, Airflow or similar
  • control systems
  • reinforcement learning problems
  • contextual bandit algos
  • scikit-learn
  • TensorFlow
  • Keras
  • PyTorch
  • software engineering guidelines including version control, testing, and automation
  • observability tools (Prometheus, Grafana, ELK, CloudWatch, Datadog)
  • cloud services such as AWS Sagemaker, Azure ML, GCP Vertex AI
  • Docker
  • Kubernetes (EKS/GKE/AKS)
  • enterprise platforms like OpenShift
  • infrastructure-as-code (Terraform, CloudFormation)
  • cloud architectures for end-to-end ML workflows on AWS
  • data science workflows
  • experiment tracking
  • feature engineering tools
  • communication skills
  • data structures
  • algorithms
  • multi-threaded programming
  • distributed computing concepts

Nice to have

  • GenAI/LLM/Agentic solutions

What the JD emphasized

  • production systems
  • large-scale datasets
  • Agentic AI solutions
  • Reinforcement Learning
  • model versioning
  • deployment strategies
  • orchestration workflows
  • performance tuning
  • automated QA pipelines
  • data drift
  • concept drift
  • system reliability
  • governance
  • security
  • compliance
  • ML pipelines

Other signals

  • Develop classifiers, predictive models and multi variate optimization algorithms
  • Design, implement and operate scalable models that can work with large-scale datasets
  • Agentic AI solutions, predictive models for conversion optimization, Reinforcement Learning, and Forecasting & Planning
  • Model Lifecycle Management
  • Review and optimize data science models, including code refactoring, containerization, deployment, versioning, and performance tuning
  • Monitor models in production
  • Ensure governance, security, and compliance for ML pipelines
Read full job description

**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

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