Back
Tier-1 brand, metro location, mid-level ML title and high visibility raise candidate competition.
Requires deep GenAI, GPU, and MLOps expertise, limiting cross-industry transferability.
Mandatory years plus specialized GPU/GenAI tooling and Kubernetes raise shortlisting strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and develop core generative AI services and APIs integrating multiple generative models into Adobe flagship products.
Build and optimize GPU-accelerated pipelines for model training and inference prioritizing performance, scalability, and reliability.
Collaborate with research and model developer teams on model inference strategies and productization, set technical direction, and mentor ML engineers.
6+ years of machine learning experience including production-scale deployments.
4+ years leading large-scale, GPU-intensive generative AI systems involving training, inference, and optimization.
Experience with PyTorch, CUDA, Triton, TensorRT, Nvidia Dynamo, Python and understanding generative architectures like diffusion models, transformers, GANs.
Work Experience Required: 6+ years in machine learning as specified above. Notice Period: Not explicitly mentioned in the JD.
Experienced in building scalable, high-performance GenAI systems for enterprise and individual users.
Skilled in design and optimization of ML workflows for enterprise-scale model customization, serving, and integration ecosystems.
Capable of collaborating across research and development teams with a focus on GPU resource management, Kubernetes, distributed systems, and MLOps.