Senior Machine Learning Engineer
Senior ML Engineer at Air builds the LLMOps backbone of its AI-native Enterprise Readiness platform for government and industrial customers: fine-tuning infrastructure, dataset and evaluation pipelines, model serving, and observability. Heavy Python, Kubernetes, and GPU-cloud work; U.S. citizenship required; Pittsburgh office or remote.
- Compensation
- Not disclosed
- Location
- Arlington, Virginia, United States; Pittsburgh, Pennsylvania, United States; Remote
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Full time
- Experience
- 5+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- U.S. Citizenship is required
- 5+ years building production machine learning systems, ML infrastructure, distributed systems, or similar
- Deep experience designing, building, and operating production ML infrastructure or ML platforms
- Experience building infrastructure for training, fine-tuning, evaluating, deploying, and monitoring LLMs or other large-scale deep learning models
- Experience with LLM fine-tuning and post-training workflows (supervised fine-tuning, LoRA/QLoRA, preference optimization)
- Strong understanding of the modern LLM lifecycle: data preparation, training, evaluation, deployment, inference, monitoring, iteration
- Experience building reproducible ML pipelines with dataset versioning, experiment tracking, model versioning, and automated evaluation
- Experience building and operating production GPU infrastructure on AWS, GCP, Azure, or dedicated GPU providers
- Strong understanding of distributed systems and running compute-intensive ML workloads at scale
- Strong programming experience in Python and building production-quality software
- Deep experience with containers, Kubernetes, and cloud platforms such as AWS, GCP, or Azure
- Experience designing scalable APIs, services, asynchronous workloads, and data-processing pipelines
- Strong understanding of observability and operational reliability for production ML systems
- Comfortable debugging failures across training code, datasets, models, GPUs, distributed systems, and cloud infrastructure
- Able to move between ML experimentation and infrastructure engineering
Nice to have
- Current U.S. security clearance, or ability to obtain one with company sponsorship
- Experience in or exposure to a startup or entrepreneurial environment
- Experience building secure code execution environments or sandboxes for AI agents
- Experience with multi-agent architectures, agent-to-agent communication, or distributed agent execution
- Experience with fine-tuning, post-training, reinforcement learning, or synthetic data generation
- Experience building AI observability, tracing, and debugging infrastructure
- Experience optimizing inference latency, throughput, GPU utilization, or model-serving costs
- Experience with AI security, adversarial testing, or securing agentic systems
- Experience working in government, defense, or other mission-critical environments
Full description
Company Description
Air is the leader in Enterprise Readiness. Our mission is to establish readiness as a real-time condition that is continuously achieved. Today, a dangerous Readiness Gap exists between what the front line needs and what is delivered. Our AI-native platform, Air Enterprise Readiness, aligns development, production, delivery, and sustainment into one coordinated execution system for government agencies and industrial suppliers. By revealing true capacity, exposing real constraints, coordinating resources, and executing at the speed of operational demands, the front line gets what it needs to succeed.
Job Description
We are seeking an experienced Senior Machine Learning Engineer to join our AI/ML team and build the infrastructure that powers the development, evaluation, deployment, and continuous improvement of our language models and AI systems.
As our AI capabilities expand, we need robust infrastructure for moving models from experimentation into production. This role will own critical parts of that lifecycle, including LLMOps, fine-tuning infrastructure, model evaluation, dataset pipelines, experiment management, model serving, and production observability.
In order to do this job well: This is an engineering-heavy ML role. You will build platforms and infrastructure that allow AI engineers and researchers to rapidly experiment with models, datasets, and training techniques while maintaining the reproducibility, scalability, and reliability required for production systems.
You will work across the full model lifecycle - from dataset creation and experimentation through training, evaluation, deployment, monitoring, and iteration.
This role is a full-time position based in our Pittsburgh, PA office or open to Remote Opportunities.
This role may require up to 25% travel, including periodic travel to our Pittsburgh, PA and Arlington, VA offices for team collaboration, planning activities, and in-person meetings.
Scope of Responsibilities
- Design and build LLMOps infrastructure supporting the development, evaluation, deployment, and continuous improvement of production language models.
- Build scalable training and fine-tuning infrastructure for commercial and open-weight language models.
- Develop pipelines supporting supervised fine-tuning, parameter-efficient fine-tuning, preference optimization, and other post-training techniques.
- Build infrastructure for distributed training and GPU-accelerated ML workloads.
- Develop data pipelines for training, fine-tuning, evaluation, and synthetic data generation.
- Build systems for dataset versioning, lineage, quality validation, transformation, and reproducible experimentation.