Cognite

Industrial data platform with AI

Senior Software Engineer - Platforms

Backend EngineeringUpdated on Nov 14

Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

At least 6 years of engineering experience building and operating production backend services at scale.
Deep expertise in JVM languages, with Kotlin preferred or Java acceptable, Python with FastAPI, distributed-systems patterns, and cloud-native service design using Kubernetes and cloud environments such as Azure, GCP, AWS, or private cloud.
Hands-on experience with workflow engines such as Conductor or Apache Airflow and event-driven architectures such as Kafka or Pub/Sub.
Experience with relational databases such as PostgreSQL, non-relational databases, data-lake object storage, and Redis caching layers in multi-tenant environments.
Practical experience with OpenTelemetry, Prometheus, and Grafana for instrumentation and operational insight.
Experience supporting production machine-learning workloads and notebooks, including job scheduling, resource management, experiment-tracking integration, or model-serving infrastructure.
Ability to build composable, well-documented, automated platform systems that enable other engineers, including machine-learning engineers, to work effectively.

Responsibilities

Design, build, and operate the core serverless execution engine and Workflows orchestration layer that underpin CDF’s AI and automation capabilities.
Own uptime, latency service-level objectives, and incident response for platform services, ensuring Functions execute deterministically and Workflows progress without data loss or silent failures.
Architect for multi-tenant, multi-cloud, high-throughput workloads, including 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 engineering teams and external customers.
Instrument services with distributed tracing, structured logging, and alerting using the OpenTelemetry, Prometheus, Grafana, or Honeycomb stack.
Lead unit, integration, and smoke-test automation and maintain deployment pipelines that deliver reliably to production.
Profile and resolve execution-throughput, cold-start-latency, and cross-service-call bottlenecks.
Model compute and storage costs for function execution and implement optimizations that reduce cloud spending without sacrificing reliability.
Build and extend platform primitives for machine-learning workloads, including compute scheduling, environment management, and secrets handling for model training, fine-tuning, and batch inference.
Support infrastructure for contextualisation pipelines, including entity matching, asset-hierarchy inference, and P&ID parsing, by enabling orchestration of long-running, GPU-aware, data-intensive machine-learning workflows.
Support vector and embedding serving or storage for semantic search and retrieval-augmented-generation contextualisation use cases.
Apply model-lifecycle awareness, including model versioning and A/B experiment tracking, while maintaining a clear boundary between platform and machine-learning framework concerns.

Desired Qualifications

Kotlin is preferred; Java is an acceptable alternative.
Familiarity with industrial knowledge-graph construction, entity resolution, or natural-language-processing and computer-vision pipelines for industrial asset data.
Familiarity with React or TypeScript for using and testing the platform’s developer tooling.
Familiarity with serving or storing vector embeddings for semantic search and retrieval-augmented-generation use cases.

What Cognite is Relentless to Achieve

Cognite operates at the forefront of industrial digitalization, building AI and data solutions that solve the world’s hardest, highest-impact problems. Our Moonshot is bold: unlock $100B in customer value by 2035, and redefine how global industry works.

About The Role

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.

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

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.
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.

Good 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.

Why Cognite

You will work at the forefront of Industrial AI, partnering with some of the world's leading industrial organisations to solve problems that actually matter at scale — not hypothetical ones. You'll join a collaborative, fast-moving environment where curiosity and ownership are the norm, not the exception. If you want delivery work you can point to and be proud of, we'd love to hear from you.

Equal Opportunity

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.

About the Company

Cognite

Industrial data platform with AI

Cognite provides cloud-based data and AI platforms for industrial companies to digitize and optimize operations. Its flagship Cognite Data Fusion connects OT, IT, and engineering data, unifying sensor data, maintenance logs, and 3D models into a real-time view of assets to improve performance and safety. Cognite Atlas AI is a low-code platform that lets engineers build AI agents for predictive maintenance, anomaly detection, and production optimization without deep coding. The company differentiates itself through deep cross-domain data integration for asset-heavy industries and a scalable cloud environment to accelerate industrial digitalization and deliver measurable value.

Company size: 501-1,000
Company stage: Acquired
Total funding: $225.3M
Headquarters: Tempe, AZ
Founded: 2016

Recently Posted Jobs

Explore more opportunities at Cognite

See more jobs