AI/ML Engineer

About Cognite & ATLAS AI

Cognite is a global industrial AI software-as-a-service (SaaS) company supporting the full-scale digital transformation of heavy-asset industries around the world. Our core product, Cognite Data Fusion (CDF), is a leading industrial DataOps platform. Building on CDF, ATLAS AI is our innovative offering designed to deliver bold, customer-centric innovation powered by artificial intelligence.

The Role

AI/ML Engineer

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 & 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 & Integration

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

• System Optimization

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

• Collaboration & Co-Innovation

  Work with solution engineers and customer teams to align models and agent behaviors with business value and industrial constraints.

What We’re Looking For

Must-Have Skills

• 4+ years of experience in AI/ML engineering, with hands-on delivery of models.
• Proficiency in working with foundation models (LLMs), including:
• Prompt engineering, evaluation, and (when relevant) fine-tuning.
• 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 labeling, 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 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 labeling industrial datasets, including annotation strategies and working with imperfect or sparse labels.