Senior Backend Software Engineer, Atlas AI

Senior Backend Software Engineer, Atlas AI

USA (Phoenix)

Cognite operates at the forefront of industrial digitalization, building AI and data solutions that solve some of the world’s hardest, highest-impact problems. With unmatched industrial heritage and a comprehensive suite of AI capabilities, Cognite accelerates the digital transformation to drive operational improvements. Our moonshot is bold: unlock $100B in customer value by 2035 and redefine how global industry works.

We thrive in challenges. We challenge assumptions. We execute with speed and ownership. If you view obstacles as signals to step forward - not step back - you’ll feel at home here. Join us in this venture where AI and data meet ingenuity, and together, we forge the path to a smarter, more connected industrial future.

We are looking for a Senior Backend Software Engineer to be a core contributor in scaling our AI capabilities. Your work will be central to building the robust, scalable backend services that power intelligent agents and integrate them into Cognite s core offerings. You will help define the architecture that brings these agents to life, ensuring they are reliable, performant, and secure.

• Build Scalable Systems
  :
  Design and implement resilient backend services and APIs that can handle high volumes of industrial data and complex AI workflows.
• Orchestrate Industrial AI
  :
  Evaluate, implement, and benchmark Large Language Models (LLMs) and AI agents, focusing on the backend infrastructure required to serve these models at scale.
• Drive Operational Excellence
  :
  Champion a "built to run" mindset by implementing comprehensive telemetry, automated testing, and CI/CD patterns to ensure our SaaS offerings meet strict SLOs.
• Collaborate Cross-Functionally
  :
  Work closely with frontend engineers, product managers, and industrial domain experts to translate complex customer needs into elegant technical solutions.

• Develop and maintain high-performance backend services primarily in Python, ensuring high code quality and test coverage.
• Build and manage infrastructure on Kubernetes across a multi-cloud environment (Azure, AWS, GCP).
• Design and optimize data schemas and access patterns for industrial time-series and relational data.
• Backend integration of generative AI capabilities, including RAG pipelines and agentic workflows.
• Participate in on-call rotations and take ownership of the production health of the services you build.
• Mentor mid-level and junior engineers through technical guidance and architectural oversight.

• 5+ years of professional software development experience, with a focus on backend systems.
• Python Expertise:
  Authoritative knowledge of Python and its ecosystem. Experience with other languages like Kotlin, Java, or Rust is a plus.
• Cloud Native Proficiency:
  Extensive experience designing and operating large-scale systems in a multi-cloud environment using Kubernetes, Docker, and Terraform.
• Distributed Systems:
  Proven track record of building scalable APIs and microservices that interact with complex data stores (SQL, No
  
  SQL, or Graph databases).

• Applied AI Interest: A strong interest in (or experience with) LLMs and AI/ML frameworks. You are excited to build the "plumbing" that makes AI useful in a production industrial setting.
• Ownership Mentality:
  You don t just write code; you take responsibility for how that code performs in production and how it solves the customer s problem.
• Communication:
  Excellent verbal and written English communication skills; able to explain complex architectural trade-offs to both technical and non-technical stakeholders.
• Experience building production-grade applications on top of LLMs (OpenAI, Anthropic, Lang Chain, etc.).
• Knowledge of Graph
  
  QL or graph databases (e.g., Neo4j).
• Familiarity with industrial protocols (OPC-UA, MQTT) or time-series databases.
• Prior experience in a high-growth SaaS environment with a strong engineering culture.
• Experience with Infrastructure as Code (e.g., Terraform) and managing containerized workloads in Kubernetes.
• Familiarity with…