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Airbus

Manufacturer of commercial aircraft, helicopters, defense

Lead AI Solutions Systems Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • A master's degree in AI (machine learning or Data Science), Computer Science, or a highly quantitative field is required.
  • At least 10 years of experience in advanced analytics initiatives, including hands-on, production-grade architecture and engineering experience in AI, and at least one Generative AI or Agentic AI system successfully designed, prototyped, and integrated into a production environment.
  • Significant experience across several business branches, including both Corporate (such as HR, Finance, or Facilities) and Operations (such as Procurement, Supply Chain, or Logistics); manufacturing experience is highly preferred.
  • Proven technical leadership designing and delivering production-ready system architectures, with experience in database modeling and implementing microservices.
  • Ability to shape technical solutions and software architectures across frontend, backend, and AI components.
  • Experience in data modeling and/or data engineering, including business ontologies, physical data models, and modern data practices.
  • Ability to frame complex business problems with non-technical stakeholders and translate them into production-ready technical specifications and data requirements.
  • Experience in both an innovation cell or startup environment and a large, matrixed global enterprise.
  • Mastery of mathematical, statistical, and algorithmic foundations of AI, from traditional machine learning to modern neural architectures, and ability to translate this knowledge into reliable, production-ready system designs.
  • Practical expertise in software development lifecycles, API contract design, containerization using Docker and Kubernetes, regression testing, and deployment pipelines. If recent agentic delivery experience is limited, active monitoring of emerging agentic patterns, LLM Wiki, and equivalent reference bodies of knowledge is required.
  • Ability to delegate responsibilities and coach junior or younger talent.
  • Strong working familiarity with SciKit and/or MLlib (PySpark) as platforms for advanced analytics, machine-learning pipelines, and operational data products; experience with PyTorch and TensorFlow.
  • Experience with Python and LangGraph or ADK; Google Enterprise Agent Platform (formerly Vertex AI) is the strategic agentic platform; familiarity with retrieval-augmented generation using any embedding.
  • Experience with Streamlit or React frontends; PostgreSQL, Firebase, or BigQuery for persistence and analytics; GCP Cloud Run for APIs; and Python as an application backend (Node/TypeScript is also possible).
  • Proficiency in assistant and agent coding across supported technologies, including Gemini CLI, Claude Code, and Codex.
  • Must be authorized to work in the United States.
  • Must be able to meet the physical requirements, including daily use of screens and office equipment, carrying items up to 10 pounds, and walking through office and production areas.
  • Required personal protective equipment includes safety shoes, safety glasses, hearing protection, respirators or masks, and/or protective gloves as required by the site or customer site.

Responsibilities

  • Evaluate the technical feasibility, data readiness, and infrastructural viability of proposed AI initiatives.
  • Translate complex business problems into rigorous mathematical, data engineering, and algorithmic requirements.
  • Define technical validation metrics, including machine-learning quality, precision, recall, system latency, data bias, drift, and large language model safety and alignment safeguards.
  • Advise business leaders on refining goals and selecting viable, scalable AI architectures.
  • Accompany business stakeholders through proof-of-concepts and industrialization, from opportunity framing to production handover.
  • Design solution blueprints, evaluate build-versus-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 and agent coding practices across the team and delivery partners.
  • Rapidly prototype initial proof-of-concepts, establish development sandboxes, and co-author code with delivery partners to accelerate industrialization.
  • Support the team's technical maturity by raising its engineering and algorithmic capabilities.
  • Anticipate, de-risk, and architect the late-2026 transition to Gemini Enterprise, including migration paths from custom-coded frameworks to no-code agentic structures.
  • Coordinate with European technical teams (AgentAiR) to align local implementations with global enterprise architectures, API standards, and corporate data governance frameworks.
  • Perform other duties as assigned.
  • Travel independently and at short notice, approximately 10% of the time.

Desired Qualifications

  • Specialization in Natural Language Processing, Generative AI, or Agentic AI.
  • Manufacturing experience.
  • Experience in agent coding or framing and supporting an assistant or agent coding practice.
  • Fluency in French.
  • Working familiarity with Palantir Foundry or Skywise.
  • Hands-on experience optimizing large language models using Vertex AI Studio on Google Cloud Platform.
  • Experience in Natural Language Processing and graph knowledge, including named entities, spaCy, FlairNLP, LLM-Wiki, hierarchical retrieval-augmented generation, Neo4j, Spanner, and GKG.
  • Experience supporting teams migrating from GitHub Copilot to Gemini CLI.

About the Company

Airbus

Manufacturer of commercial aircraft, helicopters, defense

Airbus designs and manufactures a wide range of aerospace products, including commercial airplanes, helicopters, defense systems, and space technologies. Its aircraft and rotorcraft are built with integrated airframes, propulsion, avionics, and digital systems to carry passengers, cargo, or conduct military missions, while its space division covers satellites, orbital infrastructure, launch services, and secure communications. Airbus differentiates itself with an end-to-end portfolio across commercial, defense, and space, a global manufacturing and services network, and a focus on sustainability and digital transformation. Its goal is to enable global mobility and connectivity while reducing aviation’s environmental impact through sustainable aviation and collaborative innovation.

Company size
10,001+
Company stage
IPO
Headquarters
Blagnac, France
Founded
1970

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