About this role
Engineers who thrive when treating obstacles as signals to step forward will find a natural home at Cognite, contributing to industrial digitalization initiatives in Bengaluru. The team builds core serverless execution engines, workflow orchestration layers, and data solutions designed to accelerate digital transformation for global industries. This person will focus on designing and operating scalable backend services, maintaining strict reliability standards, and building the foundational primitives that power automation and AI capabilities.
Created to drive the company moonshot of unlocking substantial customer value by 2035, the role exists to ensure platform services execute deterministically and handle high-throughput workloads without data loss. Individuals who excel here bring a strong platform-thinking spirit, enjoying the creation of composable, well-documented systems that empower other engineers and machine learning teams to move faster. Success requires a practical background in distributed systems, backend engineering, and cloud-native service design.
What you will do
- Design, build, and operate the core serverless execution engine and workflows orchestration layer for automation capabilities.
- Manage uptime, latency service level objectives, and incident response for platform services to prevent silent failures.
- Architect multi-tenant and multi-cloud systems with scheduling, queueing, and retry mechanisms for high-throughput workloads.
- Define and evolve clean API-first architecture, including versioned REST and event-driven APIs.
- Instrument services using distributed tracing, structured logging, and alerting stacks to surface failures proactively.
- Champion test automation across unit, integration, and smoke tests while maintaining deployment pipelines.
- Profile and resolve bottlenecks in execution throughput, cold-start latencies, and cross-service call chains.
What you need
- Bring three to six years of professional engineering experience building and operating production backend services at scale.
- Demonstrate deep expertise in JVM languages, with Kotlin preferred and Java acceptable, alongside Python and FastAPI.
- Possess solid understanding of distributed systems patterns and cloud-native service design across Kubernetes, Azure, GCP, AWS, or private clouds.
- Show hands-on experience with workflow engines such as Conductor or Apache Airflow, and event-driven architectures like Kafka.
- Work comfortably with relational databases like PostgreSQL, non-relational databases, object storage data-lakes, and caching layers like Redis.
- Have practical operational insight using Open-telemetry, Prometheus, and Grafana for system instrumentation.
- Maintain exposure to supporting machine learning workloads and notebooks in production environments.
Nice to have
- Model compute and storage costs for function execution to reduce cloud spend
- Familiarity with industrial knowledge graph construction, entity resolution, or NLP and CV pipelines
- Familiarity with React or TypeScript for consuming and dogfooding developer tooling
- Experience with vector embeddings for semantic search and retrieval-augmented generation use cases
Skills and tech stack
Questions candidates ask
What kind of systems and architecture will I work on?
You will design, build, and operate the core serverless execution engine and workflows orchestration layer that serve as foundational primitives for AI and automation capabilities. The architecture involves multi-tenant and multi-cloud high-throughput workloads requiring custom scheduling, queueing, and retry mechanisms.
What is the expected background and experience for this role?
Candidates should have three to six years of engineering experience with a proven track record of building and operating production backend services at scale. Required skills include mastery of JVM languages like Kotlin or Java, Python with FastAPI, distributed systems patterns, and cloud-native service design.
What observability and testing tools does the team use?
Services are instrumented with distributed tracing, structured logging, and alerting using the Open-telemetry, Prometheus, Grafana, and Honeycomb stack. The team also champions test automation including unit, integration, and smoke tests alongside reliable deployment pipelines.
The application is on the official Cognite - AI for Industry careers page.
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