AI Infrastructure Engineer

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 Utilidatas 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 Utilidatas 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 Utilidatas 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 Utilidatas 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 Utilidatas 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 individuals 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.

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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