Senior Software Engineer - Platforms

Overview

Senior Software Engineer role building high-performance, distributed platform infrastructure, including the serverless execution engine and Workflows orchestration layer that underpin Cognite's AI and automation capabilities.

What you'll do

• Design, build, and operate the core serverless execution engine and Workflows orchestration layer.
• Own uptime, latency SLOs, and incident response for platform services, ensuring Functions execute deterministically and Workflows progress without data loss or silent failures.
• Architect for multi-tenant and multi-cloud, high-throughput workloads; design scheduling, queueing, and retry mechanisms that degrade gracefully under pressure.
• Define and evolve API-first architecture, including versioned REST and event-driven APIs used by downstream teams and external customers.
• Instrument services with distributed tracing, structured logging, and alerting (OpenTelemetry / Prometheus / Grafana / Honeycomb stack).
• Champion test automation (unit, integration, and smoke tests) and maintain deployment pipelines that ship to production with confidence.
• Profile and resolve bottlenecks in execution throughput, cold-start latencies, and cross-service call chains.
• Model compute and storage costs for functions execution and implement optimizations that reduce cloud spend without sacrificing reliability.

What you'll need

• 6–8 years of engineering experience building and operating production backend services at scale.
• Deep mastery of JVM languages (Kotlin preferred, Java acceptable) and Python (FastAPI).
• Understanding of distributed systems patterns and cloud-native service design (Kubernetes, Azure, GCP, AWS, Private cloud).
• Hands-on experience with workflow engines (Conductor, Apache Airflow, or equivalent) and event-driven architectures (Kafka, Pub/Sub).
• Comfort working with relational databases (PostgreSQL) and non-relational databases, object storage (data lakes), and caching layers (Redis) in multi-tenant environments.
• Practical experience with OpenTelemetry, Prometheus, and Grafana for instrumentation and operational insight.
• Experience supporting ML workloads and notebooks in production, through job scheduling, resource management, experiment tracking integration, or model serving infrastructure.

Nice to have

• Familiarity with industrial knowledge graph construction, entity resolution, or NLP/CV pipelines as they relate to industrial asset data.
• Familiarity with React or TypeScript.
• A passion for building composable, well-documented, and automated platform systems that empower other engineers, including ML engineers.
• Support for ML workloads: build and extend platform primitives (compute scheduling, environment management, secrets handling) enabling reliable model training, fine-tuning, and batch inference.
• Support the infrastructure behind Contextualisation capabilities (entity matching, asset hierarchy inference, P&ID parsing), orchestrating long-running, GPU-aware, data-intensive ML workflows.
• Familiarity with serving or storing vector embeddings to support semantic search and RAG-based contextualisation.
• Understanding of model versioning, A/B experiment tracking, and the boundary between platform and ML framework concerns.

Details

• Location: India (Bengaluru).