Artificial Intelligence Engineer

Stelvio Inc.; Austin (TX); On-site; USD 180,000 - 240,000; Full time

Job summary

Stelvio Inc. in Austin is seeking an experienced AI engineer to own the complete model lifecycle—from data and experimentation through training, evaluation, optimization, release and production operation.

You will work hands-on to build underlying intelligence, not just connect existing services, and you will contribute across architecture and technical direction while mentoring teammates as the team grows.

Qualifications

• 5+ years of professional AI, machine learning or deep learning experience.
• Clear evidence that you have personally built, trained, fine-tuned or released models.
• Strong Python and modern deep learning framework experience.
• Experience moving models beyond experimentation into production environments.
• An understanding of the trade-offs between model quality, latency, throughput, cost and reliability.
• The ability to work independently through ambiguous technical problems.
• Experience operating in regulated, safety-conscious or high-consequence environments would be valuable.
• Senior candidates should demonstrate ownership of substantial production systems.
• Principal-level candidates should also show architectural influence, technical leadership and the ability to raise engineering standards across a team.

Responsibilities

• Design, train and improve domain-specialized language and machine learning models.
• Own fine-tuning and post-training workflows, potentially including SFT, LoRA/QLoRA, DPO, ORPO, GRPO and reinforcement fine-tuning.
• Build reproducible experimentation and evaluation frameworks covering model quality, robustness, calibration and regression.
• Develop and improve datasets used for training, alignment, benchmarking and continuous model development.
• Optimize training and inference across distributed NVIDIA GPU environments.
• Improve latency, throughput and infrastructure efficiency through batching, mixed precision, quantization, compilation and distillation.
• Productionize models through scalable APIs, containers and controlled release workflows.
• Build monitoring and observability around model behavior, performance degradation and production failures.
• Contribute to intelligent workflows combining models, policy logic, tools, agents and human review.
• Influence architecture and technical direction while mentoring other engineers as the team grows.

Skills

Python

Deep learning

Production systems

Model deployment

Distributed training

Tools

PyTorch

Docker

Kubernetes

CUDA

NVIDIA GPUs

Job description

Some AI roles begin once a model API has been selected. This one begins much earlier.

We’re supporting an Austin-based AI company building domain-specific models and intelligent decision systems for complex, high-consequence environments.

They are expanding the core technical team and looking for experienced engineers who can take ownership across the complete model lifecycle - from data and experimentation through training, evaluation, optimization, release and production operation.

This is a hands-on individual contributor position for someone who wants to build the underlying intelligence, not simply connect existing AI services.

What You’ll Work On

• Design, train and improve domain-specialized language and machine learning models.
• Own fine-tuning and post-training workflows, potentially including SFT, LoRA/QLoRA, DPO, ORPO, GRPO and reinforcement fine-tuning.
• Build reproducible experimentation and evaluation frameworks covering model quality, robustness, calibration and regression.
• Develop and improve datasets used for training, alignment, benchmarking and continuous model development.
• Optimize training and inference across distributed NVIDIA GPU environments.
• Improve latency, throughput and infrastructure efficiency through batching, mixed precision, quantization, compilation and distillation.
• Productionize models through scalable APIs, containers and controlled release workflows.
• Build monitoring and observability around model behavior, performance degradation and production failures.
• Contribute to intelligent workflows combining models, policy logic, tools, agents and human review.
• Influence architecture and technical direction while mentoring other engineers as the team grows.

You should offer meaningful depth across model development and the systems required to run models reliably in production.

What We’re Looking For

• 5+ years of professional AI, machine learning or deep learning experience.
• Clear evidence that you have personally built, trained, fine-tuned or released models.
• Strong Python and modern deep learning framework experience.
• Experience moving models beyond experimentation into production environments.
• An understanding of the trade-offs between model quality, latency, throughput, cost and reliability.
• The ability to work independently through ambiguous technical problems.
• Experience operating in regulated, safety-conscious or high-consequence environments would be valuable.
• Senior candidates should demonstrate ownership of substantial production systems.
• Principal-level candidates should also show architectural influence, technical leadership and the ability to raise engineering standards across a team.

NOTE: This role is less likely to suit you if your recent experience has primarily focused on prompt engineering, third-party LLM APIs, standard RAG implementations, analytics or MLOps without direct ownership of model development.

Why Consider It?

You’ll join a growing technical team where senior engineers can materially influence the models, architecture and engineering culture. The work sits at the intersection of applied research and production engineering, with genuine ownership over systems where accuracy, explainability, reliability and human oversight all matter.