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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,