Chief System Engineer

Stelvio Inc.; Austin (TX); Hybrid; USD 190,000 - 320,000; Full time

Job summary

Stelvio Inc. in Austin, TX, is seeking a senior technical leader to collaborate with the Chief AI Officer and shape the AI platform's direction. This role blends hands-on engineering with strategic leadership across model development, deployment, and governance.

You will drive complex ML initiatives, optimize GPU workloads, and mentor engineers while ensuring reliability and scalability in regulated environments.

Qualifications

• 10+ years of AI/ML engineering experience, with hands-on production ML systems.
• Hands-on experience training, fine-tuning and evaluating transformer or DL models.
• Advanced Python and ML frameworks (PyTorch, JAX, TensorFlow, Hugging Face).
• Experience with distributed training and high-performance GPU computing.
• Knowledge of CUDA and inference-optimization tools (Triton, TensorRT, ONNX, vLLM).
• Proven ownership of the complete model lifecycle: data prep, training, deployment, monitoring.

Responsibilities

• Partner with the Chief AI Officer to set AI strategy and priorities.
• Lead design, development and production release of ML, DL and LLM systems.
• Own decisions across model selection, training, evaluation, and deployment.
• Develop reliable AI systems for high-accuracy, regulated environments.
• Optimize GPU workloads, latency, throughput and costs.
• Establish standards for experimentation, benchmarking and governance.
• Mentor AI/ML engineers and raise engineering standards.
• Translate business needs into technical roadmaps and guide execution.

Skills

AI leadership

Python

PyTorch

Distributed training

MLOps

Model lifecycle

Cloud platforms

CUDA

Kubernetes

Tools

Docker

Kubernetes

CI/CD

CUDA

TensorRT

ONNX

Triton

vLLM

Job description

Austin, Texas | Full-time | On-Site (Hybrid)

I’m supporting a growing AI company in Austin with the appointment of a senior technical leader who will work closely with its Chief AI Officer.

This is effectively a second-in-command position within the AI engineering function. You will help define the technical direction of the company’s AI platform, lead its most complex engineering initiatives and provide technical leadership when the Chief AI Officer is unavailable.

Although senior, this remains a deeply hands‑on engineering role. We are looking for someone who can move comfortably between architecture, model development, performance optimization, production deployment and technical leadership.

The opportunity

• Act as a trusted technical partner to the Chief AI Officer, contributing to AI strategy, architecture and engineering priorities.
• Lead the design, development and production release of advanced machine learning, deep learning and LLM‑based systems.
• Own technical decisions across model selection, training, fine‑tuning, evaluation, inference and deployment.
• Develop reliable AI systems for complex, high‑accuracy and regulated decision‑making environments.
• Optimize GPU workloads, model serving, latency, throughput and infrastructure cost.
• Establish rigorous standards for experimentation, benchmarking, reproducibility, monitoring and model governance.
• Guide the development of RAG, agentic and human‑in‑the‑loop workflows where accuracy, traceability and evidence are essential.
• Mentor AI/ML engineers and raise engineering standards across the team.
• Translate commercial and operational requirements into clear technical roadmaps.
• Represent the AI engineering function in discussions with senior leadership, customers and cross‑functional teams.

What we’re looking for

• 10+ years of relevant AI, machine learning or deep‑learning engineering experience.
• A strong record of personally building and releasing production ML systems—not solely managing teams or integrating third‑party APIs.
• Hands‑on experience training, fine‑tuning and evaluating transformer or other deep‑learning models.
• Advanced Python expertise and strong experience with frameworks such as PyTorch, JAX, TensorFlow and Hugging Face.
• Experience with distributed training and high‑performance GPU computing.
• Practical knowledge of CUDA and technologies such as Triton, TensorRT, ONNX, vLLM or comparable inference‑optimization tools.
• Proven ownership of the complete model lifecycle: data preparation, experimentation, training, evaluation, deployment, monitoring and iteration.
• Strong understanding of evaluation design, benchmarking, calibration, failure analysis and reproducible experimentation.
• Experience building scalable APIs, model‑serving infrastructure and cloud‑native AI platforms.
• Familiarity with Docker, Kubernetes, CI/CD and modern MLOps practices.
• The technical judgment and communication skills required to lead complex engineering decisions and deputize for a senior AI executive.

Experience delivering AI systems in regulated or high‑consequence environments - where explainability, auditability, security and data privacy matter, would be particularly valuable.

We’re looking beyond prompt‑only orchestration. The successful person will have personally trained or fine‑tuned models, optimized their performance and operated them in production.

This is an opportunity to become one of the most influential technical leaders within a company building AI systems that must be accurate, scalable, explainable and dependable.

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