Design and implement AI Bench (AI Workbench) environments for experimentation and prototyping
Build standardized, reproducible AI development environments (notebooks, containers, IDEs)
Enable rapid prototyping using AI frameworks such as PyTorch, TensorFlow, and NVIDIA NeMo
Integrate AI benches with enterprise data platforms (Cloudera, Spark, Hadoop)
Configure and optimize GPU-enabled environments for training and experimentation
Support distributed AI workloads for research and early-stage model development
Provide self-service AI benches for data scientists and ML engineers
Implement environment versioning, dependency management, and reproducibility standards
Monitor bench usage, performance, and resource utilization
Ensure security, access control, and isolation across AI benches
Collaborate with AI Platform, Data, and MLOps teams to align bench capabilities.

5+ years of experience in AI Workbench, ML Infrastructure, or Platform Engineering roles
Strong hands-on experience with PyTorch-based experimentation environments
Experience supporting AI research and data science teams
Working knowledge of big data platforms (Cloudera, Spark, Hadoop)
Experience with GPU-accelerated environments (NVIDIA CUDA, multi-GPU setups)
Solid experience with Docker, Kubernetes, and Linux
Proficiency in Python for scripting, automation, and AI workflows
Familiarity with notebook and IDE tooling (Jupyter, VS Code, remote development)
Exposure to distributed training frameworks is a plus
Understanding of MLOps concepts is advantageous
Strong problem-solving and user-centric mindset
Excellent communication and collaboration skills
Fluent in English (written and verbal).

Senior AI Bench Engineering

Key skills