Senior Software Engineer - Work Flows

We're seeking a Senior Software Engineer who excels at building high-performance distributed systems and thrives in a fast-paced startup environment. You'll be working on cutting-edge data infrastructure challenges that directly impact how Fortune 500 industrial companies manage their most critical operational data.

Responsibilities

Platform Ownership: Design, build, and operate the core serverless execution engine and Workflows orchestration layer; Reliability Engineering: Own uptime, latency SLOs, and incident response for platform services; Scalability: Architect for multi-tenant & multi-cloud, high-throughput workloads; API Design: Define and evolve clean API-first architecture, versioned REST and event-driven APIs; Observability: Instrument services with distributed tracing, structured logging, and alerting; CI/CD & Testing: Champion test automation - unit, integration, and smoke tests and maintain deployment pipelines; Performance: Profile and resolve bottlenecks in execution throughput, cold-start latencies, and cross-service call chains; Cost Efficiency (Bonus): Model compute and storage costs for functions execution; identify and implement optimizations that reduce cloud spend without sacrificing reliability

Requirements

6–8 Years of Engineering: Proven track record building and operating production backend services at scale; Expertise: Deep mastery of JVM languages (Kotlin preferred, Java acceptable), Python(FastAPI), distributed systems patterns, and cloud-native service design (Kubernetes, Azure, GCP, AWS, Private cloud); Workflow & Orchestration: Hands-on experience with workflow engines (Conductor, Apache Airflow, or equivalent) and event-driven architectures (Kafka, Pub/Sub); Data & Storage: Comfortable working with relational databases (PostgreSQL) & non-relational databases, object storage(Data-lakes), and caching layers (Redis) in multi-tenant environments; Observability Stack: Practical experience with Open-telemetry, Prometheus, and Grafana for instrumentation and operational insight; ML Platform Exposure: experience supporting ML workloads & notebooks in production, whether through job scheduling, resource management, experiment tracking integration, or model serving infrastructure; The Platform Thinking Spirit: A passion for building composable, well-documented, and automated platform systems that empower other engineers including ML engineers to build faster

Nice to have

Contextualisation Domain: Familiarity with industrial knowledge graph construction, entity resolution, or NLP/CV pipelines; Full-Stack Awareness: Familiarity with React or TypeScript; ML Workload Support: Build and extend platform primitives compute scheduling, environment management, and secrets handling, that enable ML engineers to run model training, fine-tuning, and batch inference jobs reliably; Contextualisation Pipelines: Support the engineering infrastructure behind Cognite’s Contextualisation capabilities; Vector & Embedding Infrastructure: Familiarity with serving or storing vector embeddings to support semantic search and RAG-based contextualisation use cases; Model Lifecycle Awareness: Understand model versioning, A/B experiment tracking, and the boundary between platform concerns and ML framework concerns