- Company Name
- Cognite
- Job Title
- Senior Backend Software Engineer, Atlas AI
- Job Description
-
Job Title: Senior Backend Software Engineer – Atlas AI
Role Summary
Design, build, and maintain high‑performance, scalable backend services that power AI agents and industrial data workloads. Lead architectural decisions for microservices, APIs, and AI infrastructure, ensuring reliability, security, and meet strict SLOs in a multi‑cloud SaaS environment.
Expectations
- Deliver robust, performant, and secure backend components for large‑scale industrial AI applications.
- Own end‑to‑end service lifecycle: design, implementation, testing, deployment, monitoring, and on‑call support.
- Mentor junior engineers and influence engineering culture across cross‑functional teams.
Key Responsibilities
- Develop and maintain REST/GraphQL APIs in Python to serve industrial time‑series and relational data, ensuring high code quality and test coverage.
- Architect and deploy containerized microservices on Kubernetes (Azure, AWS, GCP) using Docker, Terraform, and CI/CD pipelines (Jenkins / GitHub Actions).
- Evaluate, benchmark, and integrate LLMs (OpenAI, Anthropic, LangChain) and generative AI pipelines (RAG, agentic workflows) into production systems.
- Design data schemas, indexing strategies, and access patterns for SQL, NoSQL, or graph databases (e.g., Neo4j) to support industrial protocols (OPC-UA, MQTT) and time‑series workloads.
- Implement telemetry, automated testing, and observability to meet SaaS SLOs, including structured logging, metrics, and alerting.
- Participate in on‑call rotations, root‑cause analysis, and service availability improvement initiatives.
Required Skills
- 5+ years professional backend development experience; strong in Python ecosystem (asyncio, FastAPI, Pydantic).
- Proficient with Kubernetes, Docker, Terraform, and multi‑cloud orchestration.
- Proven ability to design scalable APIs and microservices interacting with complex data stores (SQL, NoSQL, Graph).
- Experience building and deploying production‑grade applications using LLMs and AI frameworks.
- Solid understanding of CI/CD, automated testing, and DevOps tooling (Jenkins, GitHub Actions).
- Excellent communication (English) to explain architectural trade‑offs to technical and non‑technical stakeholders.
- Preferred: familiarity with GraphQL/graph databases, industrial protocols, time‑series databases, and AI/ML model plumbing.
Required Education & Certifications
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field.
- Preferred certifications: AWS Certified Solutions Architect, Google Cloud Professional Architect, Azure Solutions Architect, or Certified Kubernetes Administrator.