AI/ML Engineer

4 days ago

Bengaluru, India Cognite Full time

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.

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