Senior Applied AI/ML Engineer

Hi, I'm Nishya. As Head of Data and AI at Intact, I'd be delighted to hear about your interest in joining our team.

Intact, the world's leading software solutions provider for the Testing, Inspection and Certification (TIC) industry, is looking for a Senior Applied AI/ML Engineer to help build the intelligence layer for the future of global assurance.

For more than 25 years, our software has helped certification bodies, accreditation bodies and scheme owners digitize complex assurance processes. But we believe the next chapter will look fundamentally different.

Our ambition is not to add AI features to an existing product. We are building the platform that will connect operational workflows, ecosystem collaboration, proprietary data and artificial intelligence to help customers make better decisions.

As a Senior Applied AI/ML Engineer, you'll work directly inside our product squads, alongside Product Managers, Product Designers and Software Engineers. Your job won't be to experiment with AI in isolation. You'll identify where intelligence can fundamentally improve a workflow, prototype solutions quickly, and turn the ones that work into reliable production capabilities used by customers around the world.

This is one of the largest transformations in our company's history. You'll join us early enough to shape not only what we build, but how we build AI-powered products.

Sounds exciting? We'd love to hear from you.

ARE YOU READY?

• Design, build and deploy AI solutions across our core product, from RAG and document understanding to agentic workflows and predictive models.
• Work across the full lifecycle, from solution design and prototyping through to integration, deployment and continuous improvement.
• Turn our proprietary TIC data into reusable data products, working closely with our Data Engineers.
• Build MLOps pipelines that move models from experimental notebooks to production.
• Make sure our AI solutions are scalable, secure, explainable and cost-effective.
• Prove that models do what we claim, with evaluation datasets, metrics and regression tests.
• Work with Product, Design, Engineering and customers to embed intelligence into existing workflows.
• Work closely with our AI engineers through pairing and design and code reviews.

ARE YOU THE ONE?

We care more about what you've built than about which frameworks you've used. Here's what we have in mind.

Must have

• 4+ years building software professionally, including at least 2 years shipping ML or LLM systems to production that real users rely on.
• Excellent Python and software engineering practice, including testing, code review, version control, and CI/CD.
• Strong ML fundamentals. You've trained, evaluated, and deployed classical models such as classification, forecasting, or anomaly detection.
• A degree in computer science, machine learning, data science, or a related field, or equivalent practical experience.

You don't need to check every box. Here's what we have in mind.

LLM and AI systems

• RAG know-how. You've built RAG to serve a real business need and can explain it end to end, from chunking strategies through to retrieval and answer quality.
• Hands-on knowledge of vector databases, embeddings and semantic search.
• Practical experience with LLMs, prompt engineering and agentic workflows, using LangChain, LangGraph or comparable orchestration frameworks.
• Evaluation rigour. You can prove a model is doing what you claim and explain it to business stakeholders.

Running AI in production

• Real MLOps discipline. MLflow, Docker and CI/CD. You know what it takes to make models traceable and repeatable.
• Monitoring and tracing for AI systems, with tools such as Langfuse or OpenTelemetry.
• Strong experience designing and integrating RESTful APIs.
• Solid SQL and experience building data pipelines and data products.
• Cost and performance instincts. You can connect business value to the cost of running ML workloads.

How you work

• You see AI as a craft and not simply another technology.
• You look at the problem first, and you'll tell us when something simpler would do the job.
• You're naturally curious, proactive and committed to continuous learning.
• You can explain a technical trade-off to a non-technical stakeholder in language they understand.
• You're fluent in English. German is a plus.

Nice to have

• Azure, such as Azure OpenAI or AI Search, and Databricks or Microsoft Fabric.
• Knowledge graphs, ontologies, or rule-based reasoning.
• Recommendation, prediction, or decision-support systems in B2B SaaS.
• Experience in regulated industries.
• A good understanding of the GDPR and the EU AI Act, includ