Software Engineer

Greenhouse Software, Inc.; Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Cognite is seeking an experienced Platform Engineer to design, build, and operate the core serverless execution engine and workflows orchestration layer in Bengaluru. You will ensure uptime, latency SLOs, and robust incident response while scaling across multi-tenant, multi-cloud environments.

You will work with JVM languages (Kotlin/Java) and Python, plus Kubernetes and cloud ecosystems (Azure/GCP/AWS). Strong API design, observability, and cost-aware engineering are essential.

Qualifications

• 3–6 years of engineering with production backend services at scale.
• Deep mastery of JVM languages (Kotlin preferred, Java acceptable) and Python (FastAPI).
• Experience with cloud-native service design and Kubernetes, Azure, GCP, AWS or private cloud.

Responsibilities

• Design, build, and operate the core serverless execution engine and Workflows orchestration layer.
• Own uptime, latency SLOs, and incident response for platform services.
• Architect for multi-tenant and multi-cloud environments; design scheduling, queueing, retry mechanisms.
• Define and evolve clean API-first architecture with REST and event-driven APIs.
• Instrument services with distributed tracing and logging; implement Open-telemetry, Prometheus, Grafana.
• Champion test automation and deployment pipelines.
• Profile and resolve bottlenecks in execution throughput and latency.
• Model compute and storage costs; optimize to reduce cloud spend.

Skills

Kotlin

Java

Python (FastAPI)

Distributed systems

Cloud-native design

Kubernetes

Azure

GCP

AWS

Private cloud

Open-telemetry

Prometheus

Grafana

Kafka

Pub/Sub

React

TypeScript

Workflow engines

Apache Airflow

Conductor

Tools

PostgreSQL

Redis

Data lakes

Object storage

Job description

Cognite operates at the forefront of industrial digitalization, building AI, and data solutions that solve the world's hardest, highest-impact problems. With unmatched industrial heritage and a comprehensive suite of AI capabilities, including low-code AI agents, Cognite accelerates the digital transformation to drive operational improvements.

We thrive in challenges. We challenge assumptions. We execute with speed and ownership. If you view obstacles as signals to step forward - not backwards - you’ll feel right at home here.

Our Moonshot is bold: Unlock $100B in customer value by 2035, and redefine how global industry works. Join us in this venture where AI and data meet ingenuity, and together, we will forge the path to a smarter, more connected industrial future.

How you’ll demonstrate Ownership

• Platform Ownership: Design, build, and operate the core serverless execution engine and Workflows orchestration layer that serve as foundational primitives for CDF's AI and automation capabilities.
• Reliability Engineering: Own uptime, latency SLOs, and incident response for platform services ensuring Functions execute deterministically and Workflows progress without data loss or silent failures.
• Scalability: Architect for multi-tenant & multi-cloud, high-throughput workloads. Design scheduling, queueing, and retry mechanisms that degrade gracefully under pressure.
• API Design: Define and evolve clean API-first architecture, versioned REST and event-driven APIs that downstream engineering teams and external customers depend on.
• Observability: Instrument services with distributed tracing, structured logging, and alerting (Open-telemetry / Prometheus / Grafana / Honeycomb stack) so failures surface before customers notice.
• CI/CD & Testing: Champion test automation - unit, integration, and smoke tests and maintain deployment pipelines that ship to production with confidence.
• Performance: Profile and resolve bottlenecks in execution throughput, cold-start latencies, and cross-service call chains driving a "snappy" platform experience for industrial workloads.
• Cost Efficiency (Bonus): Model compute and storage costs for functions execution; identify and implement optimizations that reduce cloud spend without sacrificing reliability.

The Impact you bring to Cognite

• 3-6 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.
• Contextualisation Domain (Bonus): Familiarity with industrial knowledge graph construction, entity resolution, or NLP/CV pipelines as they relate to industrial asset data is a strong differentiator.
• Full-Stack Awareness (Bonus): Familiarity with React or TypeScript is a plus for consuming and dogfooding your own platform’s developer tooling.
• 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.
• We're globally recognized domain experts with an international presence that spans Phoenix, Houston, Oslo Tokyo, Bengaluru, and Abu Dhabi.

Nice to have

ML Platform & Contextualisation:

• 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 (entity matching, asset hierarchy inference, P&ID parsing) by ensuring the platform can orchestrate long-running, GPU-aware, and data-intensive ML workflows without manual intervention.
• Vector & Embedding Infrastructure (Bonus): 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, so the platform stays lean while ML teams stay unblocked.

Cognite is committed to creating a diverse and inclusive environment at work and is proud to be an equal opportunity employer. All qualified applicants will receive the same level of consideration for employment.