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Deloitte Autonomous Vehicle MLOps Lead, Manager - Managed AI in Kansas City, Missouri

Autonomous Vehicle MLOps Lead, Manager - Managed AI

The Team

The Deloitte Connected and Autonomous Vehicle (CAV) team is catalyzing and shaping the Autonomous Vehicle (AV) market through a suite of turnkey, as-a-service solutions that deliver improved performance and lower total cost of ownership. These solutions will empower Automotive customers to realize their autonomy ambitions as efficiently as possible.

High Level Role

We are looking for an MLOps Lead to own the technical development and production release of Deloitte's MLOps-as-a-Service, a disruptive solution that will revolutionize the world of transportation and the growing field of self-driving cars. This solution enables Automotive clients to train their AV models, accelerate DNN development efficacy, and improve data scientist productivity.

Specific tasks include:

  • Develop an ML pipeline & model management environment for building, training and inferencing models in AV development, simulation, and last mile testing

  • Ensure support of multiple opensource frameworks (e.g. TensorFlow, PyTorch) and programming languages in multi-GPU workload scenarios involving both model and/or data parallelism

  • Orchestrate and schedule multiple parallel experiments (AI models for training for example) in pooled GPU resources in a Kubernetes cluster for maximizing utilization, throughput, and priorities

  • Ensure role-based/self-provisioning of infrastructure resources for data-scientists with automated workflow (model access, build, train, simulate, last mile testing)

  • Integrate with data pipeline process for target datasets - models during training and simulation

  • Evaluate MLOps ISVs to determine build vs buy for additional features

  • Work directly with key AV customers to understand their technology and deliver the best solutions

    Qualifications:

  • Experience in HPC/AI distributed computing environments leveraging K8S orchestration and SLURM schedulers + optimization

  • Understands hybrid cloud considerations for burst capacity and run-time allocation for model training or development in the cloud vs on-prem

  • Well-versed with orchestration and scheduling of multiple parallel experiments (AI models for training for example) in pooled GPU resources in a Kubernetes cluster for maximizing utilization, throughput, and priorities

  • Experience with scalability, operations/run-time considerations for dynamic provisioning, suspend-resume, monitors and trouble-shooting model corruption and change control issues

  • Bachelor's Degree in CS or IE/Data science with 6+ years in this field. Advanced degree preferred

  • Ability to travel up to 50% on average, based on the work you do and the clients and industries/sectors you serve

  • Limited immigration sponsorship may be available

AI&DE23

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law.

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