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

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

6+ 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.