AI/ML Engineer
Job Summary
Job Description
As an AI/ML Engineer in the ATLAS AI Co-Innovation team at Cognite, you will design and optimize advanced AI models and agent architectures for industrial GenAI. This role involves working with complex, real-world industrial data, collaborating with solution engineers and product teams to build scalable AI components for next-generation industrial workflows. It requires strong AI/ML engineering skills, curiosity, and the ability to adapt cutting-edge research into high-impact solutions.
Must have:
- Design and apply foundation models to domain-specific tasks, focusing on prompt engineering, reasoning workflows, and tool use.
- Develop modular, production-ready agent workflows integrated with CDF and ATLAS AI, leveraging tools, memory, reasoning chains, and APIs.
- Evaluate and integrate new GenAI tools, open-source frameworks, and APIs into ATLAS AI workflows.
- Benchmark performance, tune retrieval and reasoning pipelines, and ensure scalability in real-world industrial deployments.
- Work with solution engineers and customer teams to align models and agent behaviors with business value and industrial constraints.
- 3+ years of experience in AI/ML engineering, with hands-on delivery of models.
- Proficiency in working with foundation models (LLMs), including prompt engineering, evaluation, 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.
- Proven ability to write clean, maintainable, and scalable code, following engineering best practices.
- A maker mindset with bias toward rapid iteration, showing rather than telling, and learning by doing.
Good to have:
- 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.
Perks:
- Join an organization of 70 different nationalities with Diversity, Equality and Inclusion (DEI) in focus.
- A highly modern and fun working environment with sublime culture across the organization.
- Flat structure with direct access to decision-makers, with minimal amount of bureaucracy.
- Opportunity to work with and learn from some of the best people on some of the most ambitious projects found anywhere, across industries.
- Join our HUB to be part of the conversation directly with Cogniters and our partners.
- Gain perks like a paid mobile telephone subscription and broadband connection.
- Get access to extended private health services with Aker Care.
- A subsidized lunch at the canteen is delivered by our chefs who specialise in options for both vegetarians and non-vegetarian, salads and hot soups every day.
- Stay fueled between meals with snacks and drinks on the house.
- Our own Cognite exclusive coffee bar with the friendliest baristas.
- Free membership to our fully-staffed gym on-site.
- Get the chance to meet Spot.
3 skills required
Job Details
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
As an 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 optimize 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 & 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):
- 3+ 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.
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