Senior Computer Scientist - ML

Adobe · Bengaluru

  • Experience11–15 yrs
  • SalaryNot disclosed
  • Work modeonsite
  • Levelexecutive
  • Posted2 Sept 2026
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About Adobe

Adobe is hiring in Bengaluru in technology software. This role looks for around 11+ years of experience.

Skills

  • Computer Science
  • Software Engineering
  • AWS
  • MLOps
  • MLflow
  • Kubeflow
  • Airflow
  • Docker
  • Kubernetes
  • OpenShift
  • Python
  • scikit-learn
  • TensorFlow
  • Keras
  • PyTorch
  • version control
  • testing
  • automation
  • data science
  • experiment tracking
  • feature engineering
  • AWS SageMaker
  • Azure ML
  • GCP Vertex AI
  • model governance
  • model lineage
  • Prometheus
  • Grafana
  • ELK
  • CloudWatch
  • Datadog
  • Terraform
  • CloudFormation

The role

A machine learning engineer at a software product company designs cloud architectures for end-to-end ML workflows and builds MLOps frameworks with Python, Docker, and Kubernetes. The work covers model governance and lineage, feature engineering, observability, and infrastructure automation.

Full job description

Requirements:

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, and 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, and 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 and lineage with tools like MLflow Registry, SageMaker Model Registry, or Vertex ML Metadata.

Familiarity with infrastructure-as-code (Terraform, CloudFormation).