Platform Engineer

The role

The Platform Engineer owns backend infrastructure end to end. You will design multi-tenant, microservices-based systems and make architectural tradeoffs regarding consistency, latency, scale, and cost.

What you will do

• Design, own, and evolve scalable microservices architectures on Kubernetes across GCP, Azure, and AWS.
• Build core platform and data-plane components in Golang and Python.
• Own service-to-service communication including gRPC/protobuf, service mesh, load balancing, retries, timeouts, and circuit breaking.
• Make and document architectural tradeoffs.
• Define the reliability contract including SLIs/SLOs, error budgets, capacity planning, autoscaling, and graceful degradation.
• Design and operate the observability stack.
• Drive DevOps and platform-engineering practices using IaC, Helm, GitOps, and CI/CD pipelines.
• Optimize for performance and cost.
• Participate in on-call rotations and lead incident response and root-cause analysis.

Required qualifications

• Bachelor's degree in Computer Science or a related field.
• 5+ years of experience in Platform, Infrastructure, or Backend Engineering.
• Strong CS fundamentals: data structures, algorithms, operating systems, and networking.
• Proficiency in Golang and Python.
• Production experience with Docker, Kubernetes, and microservices architecture.
• Hands-on experience with at least one major cloud provider (GCP, Azure, or AWS).
• Strong database expertise: query and read/write-path optimization, partitioning/sharding, replication and consistency models, with practical experience in NoSQL and graph stores.
• Solid grasp of the CAP theorem and database internals.
• Solid distributed-systems foundation: idempotency, backpressure, delivery semantics, and message queues (Kafka/Pulsar/NATS/PubSub).
• Track record of building platforms from the ground up that other engineering teams successfully build on.

Nice to have

• multi-cloud a strong plus
• Experience operating systems at high scale (high QPS, large data volumes)
• Depth in auth and security: secrets management (Vault), mTLS, RBAC, OIDC/SAML, network policy
• Experience with vector databases (pgvector/Pinecone/Milvus) and graph databases (Neo4j/Neptune)
• Open-source contributions to infrastructure projects (e.g., Kubernetes operators)

Benefits

• Certain roles may be eligible for variable compensation, equity, and benefits.

About the company

Ema builds AI Employees for HR, IT and Finance. Headquartered in Silicon Valley with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production.