Sr. Machine Learning Engineer 5

Adobe Adobe · Enterprise · Noida, India +1

This role focuses on engineering GenAI backend services for video, deploying ML models from experimentation to production, optimizing performance, and enhancing MLOps workflows. It requires expertise in Generative Video AI, model optimization, inference efficiency, and GPU acceleration for cloud-scale products.

What you'd actually do

  1. Lead the design, development, and deployment of GenAI backend services for video, building GPU‑optimized and highly efficient model pipelines.
  2. Translate research innovations and prototypes into scalable, reliable ML systems across Adobe’s cloud platforms.
  3. Optimize model and service performance, ensuring scalability, reliability, and robustness through continuous monitoring and iteration.
  4. Enhance MLOps workflows, including CI/CD, model versioning, evaluation pipelines, and automated retraining systems.
  5. Collaborate cross‑functionally with product, research, and infrastructure teams to define requirements and deliver high‑impact solutions.

Skills

Required

  • Python
  • PyTorch
  • TensorFlow
  • Generative Video AI
  • diffusion models
  • GAN models
  • transformer-based models
  • temporal consistency
  • multimodal generation
  • model optimization
  • inference efficiency
  • GPU acceleration
  • production ML integration
  • cloud-scale systems
  • data-driven products

Nice to have

  • Master’s or Ph.D. in Computer Science, AI/ML, or related field
  • B.Tech with strong, proven ML experience
  • large-scale distributed systems
  • AI/ML fundamentals, frameworks, and modern toolchains
  • technical leadership
  • collaboration skills
  • matrixed organization experience

What the JD emphasized

  • 10+ years of experience
  • Deep expertise in Generative Video AI
  • model optimization, inference efficiency, GPU acceleration, and production ML integration
  • cloud-scale, data-driven products

Other signals

  • GenAI backend services for video
  • take ML models from experimentation to full-scale deployment
  • scalable, reliable ML systems
  • Optimize model and service performance
  • Enhance MLOps workflows
  • Generative Video AI
  • model optimization, inference efficiency, GPU acceleration
  • cloud-scale, data-driven products