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Remote Data Infrastructure Jobs in Detroit, MI (NOW HIRING)

Utilidata

AI Infrastructure Engineer

Ann Arbor, MI · Remote

$170K - $210K/yr

The AI Infrastructure Engineer is responsible for designing, building, and owning the end-to-end ... data science teams and is open to fully remote candidates, with periodic travel expected for ...

Huntington

Enterprise Architect (IT)

Detroit, MI · On-site +1

$102K - $208K/yr

Facilitate alignment across application, data, integration, infrastructure, cloud, and security ... Remote roles will also have the opportunity to come together in our offices for moments that matter.

Huntington

Enterprise Architect (IT)

Detroit, MI · On-site +1

$68.25 - $88/hr

Facilitate alignment across application, data, integration, infrastructure, cloud, and security ... Remote roles will also have the opportunity to come together in our offices for moments that matter.

Huntington

Application Architect (IT)

Detroit, MI · On-site +1

$70K - $140K/yr

Experience across business, application, data, infrastructure, security, cloud, and integration ... Remote roles will also have the opportunity to come together in our offices for moments that matter.

Hitachi Careers

Public Relations Associate

MI · Remote

Our resilient, high-performance data infrastructure means that customers - from banks to theme ... Location for this position is remote. What You Will be Doing *Drive Hitachi Vantara's PR programs ...

Utilidata

Senior Software Engineer, DevOps

Ann Arbor, MI · Remote

$160K - $190K/yr

Contribute to strategic planning on how infrastructure solutions evolve to match Data Center ... remote work. Our Commitments: Utilidata values the diversity of our team. We provide equal ...

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Remote Data Infrastructure information

What is remote data infrastructure?

Remote data infrastructure refers to the systems, tools, and processes that enable organizations to collect, store, manage, and analyze data from remote locations, often via cloud-based platforms. This infrastructure allows teams to access and work with data securely from anywhere, supporting distributed work environments and scalable data solutions. It typically involves cloud storage, data pipelines, databases, and security protocols tailored for remote accessibility. Remote data infrastructure is essential for businesses that operate in multiple locations or have remote teams.

What are the key skills and qualifications needed to thrive as a remote data infrastructure engineer?

To excel as a Remote Data Infrastructure Engineer, you need a strong background in computer science, data architecture, and experience with cloud platforms such as AWS, Azure, or Google Cloud. Familiarity with tools like Terraform, Kubernetes, and data pipeline technologies, as well as relevant certifications (e.g., AWS Certified Solutions Architect), is typically required. Strong problem-solving abilities, clear communication, and self-motivation are essential soft skills for remote collaboration and troubleshooting. These competencies ensure reliable, scalable data systems and effective teamwork across distributed environments.

What are some common challenges faced by professionals working in remote data infrastructure roles?

Professionals in remote data infrastructure roles often encounter challenges such as ensuring seamless communication across distributed teams, maintaining high availability and performance of data systems, and managing security risks associated with remote access. Coordinating with colleagues across different time zones can require flexibility in scheduling and proactive communication. Additionally, remote data infrastructure engineers must stay up-to-date with evolving cloud technologies and best practices to effectively support scalable, reliable, and secure data architectures.

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Remote Data Infrastructure jobs near you

AI Infrastructure Engineer

Utilidata

Ann Arbor, MI • Remote

$170K - $210K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 11 days ago

Job description

Utilidata is a fast-growing NVIDIA-backed AI company enabling AI data centers to dynamically orchestrate power and unlock more compute capacity from existing energy infrastructure. For over a decade, we have applied AI to the electric grid — bringing real-time visibility and power-flow control to complex energy infrastructure. Our Karman platform, built on a custom NVIDIA module, brings that same capability to AI data centers, giving operators a way to better use the power already available to them.
The AI Infrastructure Engineer is responsible for designing, building, and owning the end-to-end infrastructure that serves Utilidata's AI and ML models across edge deployments, cloud environments, and data center integrations. They are also responsible for designing, building, and owning the integration of power data with AI inference software.  This is Utilidata's first dedicated role of this kind, and will serve as the foundational function for how the company deploys and operates AI capabilities in production. The role requires deep technical expertise in ML model serving, distributed systems, and GPU infrastructure, with a strong emphasis on reliability, performance, and scalability. This position works cross-functionally with product, engineering, and data science teams and is open to fully remote candidates, with periodic travel expected for company retreats and key on-site engagements.
Responsibilities
  • Lead the design and build of Utilidata's AI inference platform — establishing architecture patterns, deployment standards, and operational practices that will scale with the company
  • Own end-to-end model serving infrastructure for Utilidata's AI infrastructure (on-prem and datacenter) 
  • Build and maintain fault-tolerant, high-performance systems for serving AI models at scale, with a focus on low latency, reliability, and cost efficiency
  • Collaborate closely with algorithms engineers to integrate AI inference data and configuration with power optimization algorithms 
  • Optimize GPU utilization and inference performance across our hardware fleet, including NVIDIA accelerators central to Utilidata's edge AI platform
  • Establish MLOps best practices including CI/CD pipelines for model deployment, monitoring, and rollback across environments
  • Contribute to infrastructure roadmap decisions, including build vs. buy tradeoffs, tooling selection, and platform evolution as the team grows

Minimum Qualifications 
  • 5+ years of software engineering experience with a strong focus on AI infrastructure, backend systems, or distributed systems
  • Hands-on experience with AI model serving frameworks (e.g., vLLM, SGLang, Triton, TensorRT, TorchServe, or similar)
  • Understanding of container orchestration and cluster management (Kubernetes, Docker)
  • Experience deploying and operating infrastructure across both datacenter and on-prem environments
  • Strong knowledge of GPU workloads and the tradeoffs that come with them — you understand how inference differs from training, and why it matters
  • Proficiency in Python; C++, CUDA, Go, Rust a plus
  • Excellent communication skills and comfort working cross-functionally in a lean, fast-moving environment
  • Willingness to travel up to 10% of time 

Enhanced Qualifications (Nice to Have) 
  • Dynamo experience a plus
  • Experience with edge AI deployments or constrained compute environments
  • Familiarity with infrastructure as code (Terraform, Helm)
  • Experience with observability platforms (Datadog, Prometheus, Grafana)
  • Background in energy, utilities, or industrial IoT
  • Contributions to open-source ML infrastructure projects

Salary Range: $170,000 to $210,000 base compensation depending on experience plus stock options. Salary will be commensurate with an individual's skills, training, years of experience, and in line with internal compensation bands.
Location: This position can be performed remotely from anywhere in the United States. 
Our Commitments:
Utilidata values the diversity of our team. We provide equal employment opportunities without regard to race, color, religion, creed, sex, gender, sexual orientation, gender identity or expression, national origin, age, physical disability, mental disability, medical condition, pregnancy or childbirth, sexual orientation, genetics, genetic information, marital status, or status as a covered veteran or any other basis protected by applicable federal, state and local laws.
We are committed to:
  • Creating a diverse and inclusive workplace that is welcoming, supportive, affirming and respectful
  • Empowering employees to solve problems and work together to make a difference
  • Providing mentorship and growth opportunities as part of a collaborative team
  • A flexible work environment with flexible paid time off
  • Competitive compensation and benefits, including health, dental, vision, and employer-match 401k

 

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