AI/ML Engineer

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.