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About This Role
Join North Wind - Accelerating Hypersonic Innovation
At North Wind, we are advancing hypersonic technologies through cutting-edge research, system development, and flight testing. Our team drives innovation in high-speed aerodynamics, propulsion systems, and mission-critical components, supporting every phase of hypersonic programs - from R&D to flight testing, If you are passionate about pushing technological boundaries in a dynamic environment, we invite you to join us.
Senior GenAI Solutions Architect:
Are you an innovator who thrives on solving complex problems with Generative AI, agentic systems, and advanced deep learning? North Wind is seeking a Senior GenAI Solutions Architect who can design and scale intelligent, multi-agent pipelines that accelerate aerospace engineering workflows; ranging from automated data pipelines to reduced‑order modeling of high‑fidelity computational fluid dynamics simulations. This role focuses on building secure, scalable GenAI architectures that operate seamlessly across IL2 to IL7 cloud platforms to support defense missions and digital engineering initiatives.
North Wind is looking for a seasoned, technically strong GenAI Solutions Architect to accelerate DoD customers’ adoption of generative and agentic AI. In this role, you will lead engagements with key defense partners, understand their mission and technical requirements, and architect innovative solutions that leverage multi‑agent orchestration, advanced deep learning techniques, and secure data integration. You will collaborate across North Wind’s digital engineering and data science ecosystem, working with cross‑functional teams and customers to deliver mission‑aligned, impactful outcomes.
Key Responsibilities:
- Serving as a trusted, customer-facing technical advisor who champions GenAI and agentic solutions across IL2–IL7 environments, guiding customers through secure, cloud‑agnostic architectures.
- Define and design solutions leveraging AWS and Azure GenAI services, lead technical workshops and demonstrations, and contribute to best practices and reference architectures for advanced deep learning and digital engineering within the DoD.
- Stay current with advancements in GenAI, multi-agent systems, and aerospace-focused AI techniques is essential to driving mission success.
- Serve as a trusted advisor, becoming an evangelist for GenAI solutions both within the company and customer facing roles.
- Demonstrate deep technical expertise in cloud infrastructure development and deployments in a cloud-agnostic methodology, working alongside North Wind IT staff and infrastructure engineers to maintain capable and secure systems.
- Work with customers and teammates to define and architect solutions that leverage AWS GenAI services (Amazon Bedrock, Amazon SageMaker Studio, Amazon Titan Embeddings, etc.) to address their most critical challenges.
- Work with customers and teammates to define and architect solutions that leverage and integrate with Azure GenAI services (AI Studio, AI Foundry, etc.)
- Lead technical workshops and demonstrations with customers to showcase the capabilities of NorthWind's platform and AI technologies.
- Contribute to the development of best practices and reference architectures for GenAI in the DoD.
- Stay up-to-date on the latest advancements in GenAI and related technologies.
Qualifications:
- Strong foundation in cloud‑native architecture, deep learning, and Generative AI, with hands‑on experience designing and deploying secure solutions across IL2–IL7 environments.
- Bachelor’s degree in computer science or a related field, or equivalent experience.
- 5+ years of experience architecting and deploying cloud-based solutions.
- 3+ years of hands-on experience with machine learning technologies and frameworks (e.g., TensorFlow, PyTorch, MLFlow, etc.).
- Experience working with large datasets and data pipelines.
- Strong understanding of cloud security principles and best practices, especially IL4-7 and CMMC 2.
- Excellent communication, presentation, and interpersonal skills.
Preferred Qualifications:
- Demonstrate expertise with AWS and Azure AI services, proficiency in developing agentic and multi‑agent systems, and a proven ability to translate complex mission requirements into scalable, production‑ready architectures.
- Master’s degree in computer science or a related field.
- Experience with developing and training Generative AI models and techniques (e.g., LLMs, diffusion models).
- Experience with Amazon SageMaker Studio and other AWS machine learning services.
- Experience with Azure AI and data ecosystem tools, including Azure AI Studio, Azure AI Foundry, Microsoft Fabric, and related Azure data lake, data pipeline, and MLOps services.
- Expertise designing or optimizing reduced‑order models, physics‑informed neural networks, or other advanced deep learning techniques for aerospace or defense engineering workloads.
- Experience working in the DoD or public sector.
- Active security clearance (e.g., Secret, Top Secret).
- AWS Certified Solutions Architect – Professional certification.
- Demonstrated commitment to staying current on emerging GenAI, digital engineering, and aerospace‑focused AI advancements.
- Demonstrated ability to lead technical workshops, influence stakeholders, and drive adoption of modern GenAI practices within complex DoD organizations.
- Familiarity with secure DevSecOps practices, containerization, and infrastructure-as-code workflows commonly used in defense cloud environments.
Desired Characteristics
- Thrives in a fast‑paced, agile environment and is comfortable working within a distributed, cross‑time zone team.
- Actively engage on collaborative communication channels such as Slack, Teams, and Confluence, and are willing to jump into rapid‑response “tiger teams” when mission needs arise.
- Demonstrates a strong willingness to learn, embraces iterative development, and consistently contributes to team success.
- Think creatively to solve complex AI/ML challenges while adhering to North Wind’s security boundaries and DevSecOps principles, and they communicate effectively with engineers, program managers, and executive leadership alike.
North Wind is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status. North Wind supports safe and drug free workplace through pre-employment background checks and drug testing.
The salary range provided is a general guideline. Actual pay will depend on several factors, including, but not limited to, education, experience, training, and other applicable qualifications. North Wind is committed to pay transparency in compliance with applicable state and local laws.
All candidates must be eligible to work in the United States.
Salary Context
This $185K-$235K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At North Wind, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $185K to $235K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
North Wind AI Hiring
North Wind has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Arlington, VA, US. Compensation range: $235K - $235K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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