Federated Systems Field Engineer
- Work directly with data scientists and AI model developers to migrate data pipelines and training/inference workloads onto a federated AI platform, ensuring scalability and performance.
- Research, design, develop, and test distributed AI workflows, including data preprocessing, model training, and inference across AWS, GCP, and Azure environments.
- Provide hands-on technical support to data science teams, including troubleshooting VM, OS-level, and cloud infrastructure issues impacting model execution.
- Develop and maintain expertise in federated learning frameworks, including NVIDIA FLARE, to enable secure and efficient distributed model development.
- Ability to work independently and collaborate as part of a team
- Effective written and verbal communication skills
- Meticulous attention to detail and quality of work product
- Ability to build and sustain professional relationships
- Ability to lead projects or workstreams
- Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
- Strong interpersonal skills and professional demeanor
- Ability to meet deadlines
- Ability to provide clear guidance to others
- Bachelor's degree
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
- Must be able to obtain and maintain the required clearance for this role
- 2+ years of experience working in agile environments with hands-on involvement in testing and validating data, ML, or distributed workflows
- 2+ years of experience implementing solutions in cloud environments (AWS, GCP, or Azure), including deploying data pipelines or AI/ML workloads
- 2+ years of experience with cloud networking fundamentals, including configuring and troubleshooting network components supporting distributed systems
- 2+ years of experience supporting or contributing to AI/ML or GenAI solutions, with exposure to model architecture, training, or inference workflows
- 2+ years of experience developing in Python for data engineering, automation, or machine learning use cases
- Delivery Center Location & Travel Requirements:
- Hybrid Work Model: Operate under a hybrid system requiring residence within a commutable distance to one of the US Delivery Center locations (Gilbert, Lake Mary, or Mechanicsburg)
- Co-location Expectation: Spend up to 30% of working time co-located at an assigned office for orchestrated opportunities, including projects, practice sessions, training, and Moments That Matter at a Deloitte Delivery Center location, Geo-Hub location, approved site, or project location
- Travel Requirement: Maximum of 10% overnight travel for client or project purposes
- Relocation Requirement: If relocation is necessary, complete the move within 12 weeks from the start date to reside within a commutable distance
- 2+ years of experience refactoring and optimizing code for performance, scalability, and maintainability in cloud or distributed environments
- 2+ years of experience with identity and access management concepts in cloud or federated environments
- 2+ years of experience working with modular cloud platforms, orchestration tools, or model control processes supporting ML pipelines
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