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About This Role
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Job Description:
Airbus Digital is looking for a Lead AI Solutions Systems Engineer to join the team. This role can be based in Herndon, VA, Atlanta, GA, Sunnyvale, CA or Mobile, AL.
Meet the Team:
As a multinational company with a global footprint, our business needs protection from security threats and assurance that our end\-users (employees, customers and suppliers) have access to the tools and data they need, when they need it. Our partners depend on us to be reliable and secure. Our information management professionals are highly motivated, dynamic and diverse \- we value collaboration, teamwork, solidarity, helping each other, achieving results and always putting Airbus first.
Your Working Environment:
The Washington, D.C. metro area is home to multiple Airbus offices: In our nation’s capital you will find the Airbus Experience Center, a collection of interactive, multimedia exhibitions highlighting the extensive role the company plays in the aviation, aerospace and defense industries in the U.S. and around the world. The D.C. area is also home to our regional corporate headquarters – located adjacent to Washington Dulles International Airport (IAD) \- it makes flying in a breeze!
The Lead AI Solutions Systems Engineer is the lead technical architect and solution designer in the North America AI and Advanced analytics Product Service Line. This role will support the Head of digital AI and Advanced analytics to drive the technical strategy, data modeling, and high\-level solution design from proof\-of\-concept (PoC) to industrialization. While the European program AgentAiR drives the end\-to\-end technology and harnessing framework (from platforms and patterns to change management), the North American node positions itself as the reference partner for business value qualification and delivery securitization.
As a Lead AI Solutions Systems Engineer, you will act as a deeply technical, hands\-on, and business\-facing lead architect and builder. You will serve as the principal technical interlocutor for business stakeholders on what AI can credibly do, how to translate business problems into mathematical and data specifications, how to architect the integration layers, and how to build and bring systems safely to production.
Your Challenges:
1\) Technical Solution Strategy \& Opportunity Framing: 40%
- Evaluate the technical feasibility, data readiness, and infrastructural viability of proposed AI initiatives.
- Act as the principal technical translator, formalizing complex business problems and translating them into rigorous mathematical, data engineering, and algorithmic requirements.
- Define and design technical validation metrics, including ML quality, precision, recall, system latency, data bias, drift, and LLM safety/alignment safeguards.
- Act as a trusted advisor to business leaders, helping them refine their goals and steer them toward highly viable, scalable AI architectures
- Accompany the business throughout PoCs and their industrialization, from opportunity framing to production hand\-over
2\) High\-Level Solution Architecture \& Prototyping: 30%
- Shape the technical response: design solution blueprints, evaluate build\-vs\-buy trade\-offs, and design staging plans.
- Collaborate with the AI Lead Developer to define local software development guidelines, API schemas, and MLOps release pipelines
- Lead and frame assistant/agent coding practices across the team and its delivery partners, ensuring robust engineering discipline.
- Act as an active, hands\-on 'doer': rapidly prototype initial Proof of Concepts (PoCs), establish development sandboxes, and actively co\-author code alongside delivery partners to accelerate industrialization
3\) Technology Evolution \& Platform Governance: 30%
- Support the technical maturity ramp\-up of the team, raising the engineering and algorithmic capabilities of team members.
- Actively anticipate, de\-risk, and architect the late\-2026 transition to Gemini Enterprise, designing the migration paths from custom\-coded frameworks to robust no\-code agentic structures.
- Coordinate technically with European technical teams (AgentAiR) to align local implementations with global enterprise architectures, API standards, and corporate data governance frameworks.
4\) Additional Responsibilities: 10 %
- Other duties as assigned
Your Boarding Pass:
- Required: Master's degree in AI (ML / Data Science), Computer Science, or a highly quantitative field.
- Preferred: Specialization in NLP (Natural Language Processing), Generative AI, or Agentic AI.
Experience:
Required:
- Minimum of 10 years of experience in advanced analytics initiatives, including mandatory hands\-on, production\-grade architecture and engineering experience in AI, and at least one Generative AI / Agentic AI system successfully designed, prototyped, and integrated into a production environment
- Significant experience across several business branches—Corporate (e.g., HR, Finance, Facilities) AND Operations (e.g., Procurement, Supply Chain, Logistics) at a minimum, with manufacturing experience highly preferred.
- Proven technical leadership in designing and delivering production\-ready system architectures, with experience in database modeling and implementing microservices
- Proven ability to shape both technical solutions and software architectures (frontend, backend, AI components).
- Robust experience in data modeling and/or data engineering, showing a deep understanding of business ontologies, physical data models, and modern data practices.
- Proven ability to frame complex business problems with non\-technical stakeholders, translating complex business problems into clear, production\-ready technical specifications and data requirements.
- Experience working in an innovation cell or startup environment, combined with experience in a large, matrixed global enterprise.
Preferred
- Experience in agent coding, or in framing and supporting an assistant / agent coding practice.
Travel Required:
- 10%
Citizenship: Authorized to Work in the US
Knowledge, Skills, Demonstrated Capabilities:
Required:
- Technical \& Scientific Rigor: Mastery of the mathematical, statistical, and algorithmic foundations of AI—from traditional ML to modern neural architectures—and the ability to translate this knowledge into reliable, production\-ready system designs
- Software Engineering Blueprinting: Deep, practical expertise in software development lifecycles (SDLC), API contract design, containerization (Docker, Kubernetes), regression testing, and deployment pipelines. If recent agentic delivery experience is limited, the candidate must demonstrate active monitoring of emerging agentic patterns, LLM Wiki, and equivalent reference bodies of knowledge.
- Value\-Driven Mindset: Prioritizes real\-world business outcomes and ROI over algorithmic sophistication.
- Open\-Mindedness : Willingness to revisit established solutions when new, cheaper, or more efficient options emerge.
- Organizational Tolerance : Highly comfortable navigating complex, global corporate structures, data governance constraints, and dependencies tied to Europe\-led solution deployments.
Preferred:
- Fluent in French is a plus
Communication Skills:
Required:
- Active Listening: Exceptional active listening capabilities, with highly didactic and illustrative communication skills to guide business teams toward shared understanding.
- Influence \& Collaboration: Strong ability to defend a solution choice credibly with both business and technical stakeholders, build consensus, and bring teams along.
- Coaching \& Delegation: Able to delegate responsibilities and actively coach junior/younger talents to build overall team capability.
Technical Systems Proficiency:
Required:
- Advanced Analytics \& ML Platform : Strong working familiarity with SciKit and/or MLlib (PySpark) and as the reference platform for advanced analytics, ML pipelines, and operational data products. PyTorch, TensorFlow
- Generative \& Agentic AI Stack : Python/Langgraph or ADK (Google Enterprise Agent Platform (formerly Vertex AI) is the strategic agentic platform), RAG (any embedding)
- Devops \& Cloud : Streamlit / React frontends, PostgreSQL / Firebase / BigQuery for persistence and analytics, GCP Cloud Run (serverless containers) for APIs, Python as application Backend (Node/Typescript possible)
- AI\-Assisted Coding Tools : Proficiency in assistant coding and agent coding across supported technologies (Gemini CLI, Claude Code, Codex)
Preferred:
- Working familiarity with Palantir Foundry / Skywise
- Hands\-on experience optimizing LLM models using Vertex AI Studio on GCP
- Experience in NLP and graph Knowledge: Named entities, Spacy, FlairNLP, LLM\-Wiki / Hierarchical RAG, Neo4j, Spanner, GKG
- Experience in supporting teams to migrate from Github Copilot to Gemini CLI
Physical Requirements:
- Onsite: Hybrid
- Vision: Daily able to see and read computer screen and other electronic equipment with screens, able to read documents, reports and engineering drawings.
- Hearing: Daily able to hear to participate in conversations in person and via teleconference or phone and to hear sounds on production floor including safety warnings or alarms.
- Speaking: Daily able to speak in conversations and meetings, deliver information and participate in communications.
- Equipment Operation: daily use of personal computer, telephone, copies, fax machine, and related office equipment and using electronic identification card to enter building floors.
- Carrying: Daily able to carry documents, tools, drawings, electronic equipment up to 10lbs.
- Sitting: Daily able to sit for long periods of time in meetings, working on computer.
- Standing: Daily able to stand for discussions in offices or on production floor.
- Travel: 10% of time able to travel independently and at short notice.
- Walking : Daily able to walk through office and production areas including uneven surfaces.
- Personal Protective Equipment required: Required PPE includes, but is not limited to, Safety Shoes, Safety Glasses, Hearing Protection, Respirators/Masks, and/or Protective Gloves as required by site and/or customer site
Equal Opportunity:
*All qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, age, marital status, pregnancy, genetic information, or other legally protected status*
*As a leader in our field, Airbus provides relocation assistance for qualified positions and a comprehensive compensation and benefits package.*
*As a matter of policy, Airbus does not sponsor visas for US positions unless specified. Only applicants with current work authorization will be considered.*
*Airbus does not offer tenured or guaranteed employment. Employment with Airbus is at will, meaning either the company or the employee can terminate the employment relationship at any time, with or without cause, with or without notice.*
*NOTE: Airbus reserves the right to revise or change job duties and responsibilities as the need arises. This position description does not constitute a written or implied contract of employment.*
This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth.
Company:
Airbus Americas, Inc.
Employment Type:
US \- Direct Hire
Experience Level:
Professional
Remote Type:
Flexible
Job Family:
Digital \<JF\-IM\-DI\>
\-
Job Posting End Date: 06\.25\.2026
\-
Airbus provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetics, pregnancy, marital status, veteran status or other legally protected status. In addition to federal law requirements, Airbus complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, demotion, termination, layoff, recall, transfer, leaves of absence, compensation, benefits and training. Airbus expressly prohibits any form of workplace harassment based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetics, pregnancy, marital status, veteran status or other legally protected status. As a matter of policy, Airbus does not sponsor visas for US positions unless specified. Only applicants with current work authorization will be considered. Airbus does not offer tenured or guaranteed employment. Employment with Airbus is at will, meaning either the company or the employee can terminate the employment relationship at any time, with or without cause, with or without notice. Airbus reserves the right to revise or change job duties and responsibilities as the need arises. This position description does not constitute a written or implied contract of employment.
By submitting your CV or application you are consenting to Airbus using and storing information about you for monitoring purposes relating to your application or future employment. This information will only be used by Airbus.
Airbus is committed to achieving workforce diversity and creating an inclusive working environment. We welcome all applications irrespective of social and cultural background, age, gender, disability, sexual orientation or religious belief.
Airbus is, and always has been, committed to equal opportunities for all. As such, we will never ask for any type of monetary exchange in the frame of a recruitment process. Any impersonation of Airbus to do so should be reported to [email protected] .
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Airbus, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Airbus AI Hiring
Airbus has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Herndon, VA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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