AI Infrastructure Engineer

Karman, Inc.; Northern (KY); Hybrid; USD 170,000 - 210,000; Full time

Benefits offered by this job

Flexible PTO

Health benefits

401k match

Job summary

Karman, Inc. is seeking an AI Infrastructure Engineer to design and own the end-to-end AI inference platform across edge, cloud, and on-prem deployments. The role emphasizes reliability, performance, and scalability for production AI workloads.

You will work cross-functionally with product, engineering and data science teams, with remote work options and occasional on-site engagements. 5+ years in AI infra is preferred.

Qualifications

• 5+ years of software engineering focusing on AI infrastructure or distributed systems.
• Hands-on with AI model serving frameworks (e.g., vLLM, SGLang, Triton, TensorRT, TorchServe).
• Experience with container orchestration (Kubernetes, Docker).
• Experience deploying infra across datacenter/on-prem environments.
• Strong knowledge of GPU workloads and inference vs training.
• Proficient in Python; CUDA, C++, Go, Rust a plus.
• Excellent cross-functional communication; travel up to 10%.

Responsibilities

• Lead design and build of AI inference platform architecture and standards.
• Own end-to-end model serving infrastructure (on-prem and datacenter).
• Build fault-tolerant, high-performance systems for scalable AI inference.
• Collaborate with algorithms engineers to integrate inference data with power optimization.
• Optimize GPU utilization and inference performance across hardware fleet.
• Establish MLOps practices including CI/CD for deployment and monitoring.
• Contribute to roadmap decisions on build vs. buy and tooling.

Skills

AI infrastructure

Backend systems

Distributed systems

Python

C++

CUDA

Go

Rust

Kubernetes

Docker

Communication

Travel readiness

Tools

vLLM

SGLang

Triton

TensorRT

TorchServe

Terraform

Helm

Job description

Karman 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

Karman 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