AI/ML Engineer

Key Responsibilities:

• End-to-End Prototyping: Build cross-stack prototypes using ATLAS AI, CDF, and open-source AI frameworks to solve real customer challenges.
• Agent Workflow Design: Design and implement multi-agent workflows that combine Large Language Models (LLMs), tool use, and reasoning over industrial data.
• Tech Exploration & Integration: Evaluate and integrate new Gen AI 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.

Required Skills & Qualifications:

• AI/ML Engineering Experience: Minimum of 3+ years of experience in AI/ML engineering, with hands-on delivery of models.
• Foundation Models (LLMs): Proficiency in working with foundation models.
• Python Skills: Strong proficiency in Python, with experience using frameworks such as LangChain, Transformers, or similar.
• Cloud-Native Development: Understanding of cloud-native development, model training workflows, and ML pipeline orchestration (e.g., data labeling, feature selection, model retraining).
• Coding Best Practices: Proven ability to write clean, maintainable, and scalable code, following engineering best practices for testing, version control, and code review.
• Maker Mindset: A bias toward rapid iteration, learning by doing, and showing solutions rather than just telling.

Bonus Skills:

• Experience with Cognite Data Fusion (CDF): Familiarity with Cognite Data Fusion (CDF) is a plus.
• Integration with Industrial Data: Experience integrating AI workflows with time series, asset hierarchies, or knowledge graphs.
• Deep Learning/Traditional ML: Knowledge of model architecture selection, hyperparameter tuning, and evaluation pipelines for both deep learning and traditional ML.
• Industrial Data Types: Understanding of industrial data types such as time series, contextual events, and industrial knowledge graphs.
• Data Labeling: Experience with labeling industrial datasets, including annotation strategies and handling imperfect or sparse labels.