Lead Software Engineer - AI

Job Description:

Job Description

Date: Jul 2025

Role: Lead Software Engineer - AI

No of Positions:1

Description:

The area of Emerging technologies for Scientific Computing in Airbus provides engineering with state of the art platforms & services to develop, prototype AI solutions for engineering and is a key enabler in the aircraft development of today and tomorrow. In our multifunctional team setup we are tackling the future challenges of the transformation of the A/C design ecosystem. We work in a multidisciplinary team setup in the context of Agile/SAFe.

We are seeking to enhance our team with a Lead AI Engineer with the main focus on Artificial Intelligence and Machine Learning in order to support the sizing and setting-up of future digital AI services and services for engineering.

This position will bring you numerous and exciting challenges. You will benefit from working with vibrant and diverse teams of IT professionals and engineers, developing your skills through extensive dedicated training programs, opportunities to travel and you will be empowered to make the difference!

Qualification & Experience:

Generative AI & Machine Learning Expertise

• Engineering graduates with 8-10 years of experience in engineering software applications (Design, development, infrastructure setup, support etc), with minimum 4-5 years of experience in AI field.

• Strong understanding of software development, design concepts and principles.

• Proven track record of architecture, design, building and maintaining applications at scale for end to end implementation.

• Generative AI (GenAI): In-depth knowledge of Generative AI concepts, Large Language Models (LLMs), and staying current with their updates.

• Retrieval-Augmented Generation (RAG): Specific experience in backend development for RAG.

• LLM Frameworks: Hands-on experience with LLM frameworks and APIs, including Hugging Face, LangChain, and OpenAI APIs.

• Multi-Agent Systems: Proven experience with multi-agent AI frameworks like Crew AI, AutoGen, Anthropic, and LangGraph, with the ability to architect scalable solutions using these systems.

• Cloud AI Services: Experience with key cloud AI services, specifically AWS Bedrock and GCP Gemini Workspace.

• AWS Sagemaker: Mandatory experience building solutions and managing machine learning workloads with AWS Sagemaker.

• Data Platforms: Desired experience with data platforms such as Databricks, Snowflake, or Palantir.

• ML Lifecycle Management: Mandatory experience in the full lifecycle of machine learning, from model development and deployment to ongoing management.

• API Integration: Experience with REST API integration across multi-cloud and hybrid environments (AWS SDK, GCP APIs).

• Desirable Skills: Knowledge of deep learning frameworks like TensorFlow, PyTorch, and Keras. Experience with Natural Language Processing (NLP) models, including transformers like BERT and GPT.

• Certifications: Professional-level cloud certifications are highly desirable, such as AWS Certified Solutions Architect – Professional, Google Cloud Certified Professional Architect, or other specialized AI-focused certifications like the Certified AI Solution Architect (CAISA).

• Strong knowledge with regard to the set-up and operation of MLOps infrastructure and services and the necessary skills to perform the following tasks:

  • Install and test containerized solutions based on appropriate tool-sets

  • Set-up an environment for proper orchestration and scheduling of jobs / ML experiments, trainings, etc.

  • Test, provide and integrate tools relevant for AI/ML services such as Docker, Jupyter Hub, Jenkins, Elastic, Spark, Scala, Kafka, Artifactory, Grafana

• Good Linux skills, preferably on Redhat 7.x,Redhat 8.x,Redhat 9.x, Centos, and Ubuntu

• Working with and migrating complex solutions to AWS/GCP infrastructure

Professional & Soft Skills

• Collaboration: Ability to work closely with central architecture teams and business domains to design end-to-end solutions.

• Stakeholder Engagement: Strong willingness to engage with internal customers and stakeholders to gather feedback, provide guidance, and support the AI/ML environment.

• Project Management: Experience with Agile methodologies and tools such as Jira or VersionOne.

• Service Management: Familiarity with IT Service Management best practices and tools like ServiceNow.

• Travel: Willingness to travel for both short and long periods to Europe.

Responsibilities:

• Lead GenAI Strategy: Architect and implement end-to-end Generative AI solutions using LLMs.

• Design RAG and Multi-Agent Systems: Develop the backend for RAG and architect scalable solutions with multi-agent frameworks like Crew AI and AutoGen.

• Manage Cloud AI Services: Utilize cloud-native platforms to build and manage AI solutions.

• Oversee ML Lifecycle: Handle the full machine learning lifecycle, from development to deployment and maintenance.

• Act as a Consultant and Mentor: Serve as a technical consultant for engineering use case owners, providing guidance and mentorship to the team on GenAI and ML best practices.

• Integrate Data and APIs: Connect solutions to data platforms and ensure smooth API integration across hybrid environments.

• Feasibility Assessment: Assess AI use cases for technical viability and business impact to support strategic decision-making.

• Solution Design: Architect end-to-end AI solutions, ensuring seamless integration across data, models, and infrastructure.

• Foundational Capabilities: Develop core AI frameworks and infrastructure to enable scalable model development and deployment.

• Customer Engagement and Feedback: Engage with internal customers to promote resource utilization and gather feedback to continuously improve the AI/ML environment.

• Agile Collaboration: Actively participate in daily scrum meetings, PI planning workshops, and collaborate with agile teams.

• Intercultural Communication: Serve as a liaison between engineering teams in Europe and India, comfortably interacting with French, German, and Spanish-speaking teams.

• Flexible Work Schedule: Be willing to work European shift times (up to 10:30 PM IST in summer and 11:30 PM IST in winter).

Other responsibilities may include:

• Perform scaling tests of integrated tools on single cores, multiple cores, multi nodes to help engineers using the software most efficiently

• Support in resolution of Level 3 incidents and perform problem analysis on IS/IT level caused by the AI applications

• Create test repositories for any new tool development

• Design, build and run automated tests

• Test and deploy the scientific computing software after patch deployments.

• Apply DevOps tools, culture and mindset in proposed solutions

• Interact with engineers to enlarge their request quality to required level

• Close collaboration with business workflow designers and implementation/integration

• Participating in escalation meetings when needed to find root causes in a large environment

• Deliver Level 3 support to engineering end users

Success Metrics:

Success will be measured in a variety of areas, including but not limited to

• Agile mind-set, collaborative way of working, quick reaction in case of operational issues, SLA fulfillment & service availability

• Consistently ensure the on-time delivery and quality of the projects

• Bring innovative cost effective solutions.

• Achieve customer satisfaction.

• Ability to handle a subject from demand management, to development, integration, maintenance and support.

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.

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