Cognite
Senior Software Engineer - AI/ML ( Platform )
Skills
1 month ago
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Important information
Contract type:
Permanent
Location:
Bengaluru, Karnataka, India
Work mode:
Onsite
Published on:
23 September 2026
What they need
About the Role:
We are looking for a Senior Software Engineer to lead the production systems for our Context Services team. You will serve as the bridge between our Applied Scientists/AI Engineers and the central Platform Engineering team. In this role, you will focus on turning complex analytical solutions—ranging from traditional ML/DL models to modern LLM agents—into highly reliable, production-grade software services.
The Impact you bring to Cognite
Key Responsibilities:
- Architecture & Scale: Design robust system architectures and develop low-latency APIs/services capable of supporting thousands of concurrent users with sub-second response times.
- Cross-Functional Problem Solving: Collaborate closely with product, architecture, and AI teams to translate complex industrial data problems into scalable solutions.
- AI & LLM Orchestration: Architect frameworks for model serving, vector databases, and agentic workflows (LLM agents), ensuring reliable state management and prompt tracking.
- Platform Integration: Act as the technical liaison with the central Platform team, leveraging core infrastructure to build reliable AI pipelines and workflows.
- Innovation: Drive technical innovation through research, prototyping, and potential contributions to open-source initiatives.
Qualifications & Skills:
- Experience: 6–10 years of industry experience in Software Engineering, Backend Engineering, or Systems Engineering, with exposure to AI, ML, or LLM-based production systems.
- Programming: Strong proficiency in Python and experience building high-concurrency applications, asynchronous programming, or REST/gRPC APIs.
- Systems: Solid understanding of containerization (Docker) and modern deployment environments (such as Kubernetes or cloud platforms).
- AI Knowledge: Familiarity with the broader AI lifecycle—including traditional model registries, vector databases, or LLM evaluation frameworks—is a major plus, but a strong ability to learn new paradigms is essential.
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