Description :

As a Senior AI/ML Engineer in the ATLAS AI Co-Innovation team, you will help push the technical boundaries of what's possible with industrial GenAI. You'll design and optimise advanced AI models and agent architectures that interact with complex, real-world industrial data.

You'll operate at the technical core of customer-facing coinnovation, working closely with solution engineers, product teams, and customer data to build smart, scalable AI components that power next-generation industrial workflows. This role demands strong AI/ML engineering skills, deep curiosity, and the ability to adapt cutting-edge research into usable, high-impact solutions.

Responsibilities :

- Model Development and Application : Design and apply foundation models to domain-specific tasks, focusing on prompt engineering, reasoning workflows, and tool use with attention to accuracy, robustness, and real-world applicability.

- Agent Architecture Design : Develop modular, production-ready agent workflows integrated with CDF and ATLAS AI, leveraging tools, memory, reasoning chains, and APIs.

- Tech Exploration and Integration : Evaluate and integrate new GenAI tools, open-source frameworks, and APIs into ATLAS AI workflows.

- System Optimisation : Benchmark performance, tune retrieval and reasoning pipelines, and ensure scalability in real-world industrial deployments.

- Collaboration and Co-Innovation : Work with solution engineers and customer teams to align models and agent behaviours with business value and industrial constraints.

Requirements :

- 5+ years of experience in AI/ML engineering, with hands-on delivery of models.

- Proficiency in working with foundation models (LLMs), including :

1. Prompt engineering, evaluation, and (when relevant) fine-tuning.

2. RAG pipelines and integration with knowledge bases or vector databases.

- Strong Python skills with experience using frameworks such as LangChain, Transformers, or similar.

- Understanding of cloud-native development, model training workflows, and ML pipeline orchestration (e. g., data labelling, feature selection, model retraining).

- Proven ability to write clean, maintainable, and scalable code, following engineering best practices for testing, version control, and review.

- A maker mindset with a bias toward rapid iteration, showing rather than telling, and learning by doing.

Bonus Skills :

- Experience with Cognite Data Fusion (CDF).

- Experience integrating AI workflows with time series, asset hierarchies, or knowledge graphs.

- Deep learning or traditional ML background (e. g., model architecture selection, hyperparameter tuning, evaluation pipelines).

- Understanding of industrial data types (e. g., time series, contextual events, industrial knowledge graphs).

- Experience in labelling industrial datasets, including annotation strategies and working with imperfect or sparse labels.