Artificial Intelligence Engineer

Thales
Crawley
1 month ago
Applications closed

Related Jobs

View all jobs

Computer Vision and Artificial Intelligence Engineer

Research Software Engineer: Geospatial Artificial Intelligence (Geo-AI)

Software Engineer, Applied Artificial Intelligence (AI)

Software Engineer, Applied Artificial Intelligence (AI)

Geospatial Artificial Intelligence Research Scientist

Artificial intelligence (AI) contract roles

About the Company

Thales UK is committed to delivering high-impact AI capabilities across its businesses and to customers, enhancing the quality of offers, winning new business, and improving customer satisfaction.


About the Role

As part of a growing software and AI team in CortAIx Factory, the AI Engineer will collaborate with product owners, domain experts, data engineers, and software engineers to turn business problems into robust, secure, and scalable AI solutions.


Responsibilities

  • Translate business needs into AI solution designs, clear requirements, and measurable success criteria.
  • Design, implement, and evaluate ML/AI models (e.g., classical ML, deep learning, computer vision, NLP/LLMs, time‑series).
  • Build robust training and evaluation pipelines, including data preprocessing, feature engineering, augmentation, and experiment tracking.
  • Ensure Responsible AI practices: model robustness, safety, explainability (e.g., SHAP/LIME), bias assessment, and alignment with MOD/regulatory requirements.
  • Package AI models as secure services/APIs and collaborate with software engineers to productionise, monitor, and continuously improve models.
  • Define operational metrics and feedback loops for model performance, data quality, and drift; support post‑deployment reviews.
  • Write secure, high‑quality production code, unit/integration tests, and conduct peer code reviews.
  • Create reusable AI components, templates, and reference implementations; contribute to the internal catalogue of capabilities.
  • Support bids, PoCs, demos, and stakeholder workshops; communicate technical concepts to non‑technical audiences.
  • Work with data engineers and architects on data acquisition, labelling strategies, integration of third‑party data, and data quality management.
  • Participate in agile threat modelling and vulnerability management for AI features; adopt best practices for secure AI.
  • Horizon scan for major AI technology trends; run trials and share best practices to accelerate responsible adoption.

Qualifications

  • 5+ years’ experience delivering AI/ML solutions in complex, safety‑ or mission‑critical domains (e.g., defence, aviation, rail, medical, or similar).
  • Proven track record of taking AI projects from discovery through model development to production handover, with measurable outcomes.
  • Significant hands‑on experience in at least one area: deep neural networks, computer vision, or time‑series analytics.
  • High‑quality technical documentation and stakeholder communication.
  • Collaboration within cross‑functional engineering teams.

Required Skills

  • Strong Python programming skills; proficiency with modern software engineering practices (testing, code quality, CI).
  • Expertise in ML/DL algorithms and techniques for supervised, unsupervised, and, where relevant, reinforcement learning.
  • Experience with AI frameworks and libraries: PyTorch, TensorFlow, scikit‑learn, Hugging Face Transformers; OpenCV for vision.
  • Experiment tracking and reproducibility tools (e.g., MLflow, Weights & Biases).
  • Data wrangling and analysis (Pandas, NumPy, SQL); familiarity with Spark or similar is a plus.
  • Model optimisation and deployment fundamentals: ONNX, TorchScript, FastAPI/gRPC; GPU acceleration (CUDA basics desirable).
  • Responsible AI and security awareness: explainability, privacy‑preserving methods (e.g., differential privacy, federated learning), adversarial robustness.
  • Proficient with Git and collaborative development workflows.
  • Awareness of Agile and DevOps principles; ability to work effectively with MLOps for production deployment.
  • Knowledge of cloud AI services (AWS/Azure/GCP) and containers (Docker) is desirable.

Preferred Skills

  • Governance of architecture or detailed designs throughout the project lifecycle.
  • Experience with large‑scale data initiatives, data labelling strategies, and data quality management.
  • Familiarity with MLOps practices and cloud platforms for AI deployment.
  • Contributions to open‑source AI projects, publications, or patents.

Pay and Compensation

Competitive salary based on experience and qualifications.


Equal Opportunity Statement

Thales UK is committed to diversity and inclusivity in the workplace, ensuring equal opportunities for all candidates.


Job Details

Seniority level: Mid‑Senior level


Employment type: Full‑time


Job function: Information Technology and Engineering


Industries: Defense and Space Manufacturing, Medical Equipment Manufacturing, and Aviation and Aerospace Component Manufacturing


Location: Crawley, England, United Kingdom


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.