The RoleYou are an experienced Platform Engineer who owns backend infrastructure end to end. You design multi-tenant, microservices-based systems that other engineering teams build on, and you make deliberate architectural tradeoffs around consistency, latency, scale, and cost. You are comfortable going deep - service mesh internals, database internals, distributed-systems failure modes - and equally comfortable defining the reliability and security contracts an enterprise AI platform depends on.
What You'll Own- Design, own, and evolve scalable microservices architectures on Kubernetes across GCP, Azure, and AWS, including multi-tenant isolation (namespaces, network policies, per-tenant resource quotas and RBAC).
- Build core platform and data-plane components in Golang and Python - data ingestion, knowledge-base indexing and vector/graph search, application connectivity, workflow automation, and ML operations - against explicit latency and throughput SLOs.
- Own service-to-service communication: gRPC/protobuf API contracts, service mesh (Istio/Linkerd), load balancing, retries, timeouts, and circuit breaking.
- Make and document architectural tradeoffs - partitioning/sharding strategy, consistency models (strong vs. eventual), caching tiers, and build-vs-buy decisions.
- Define the reliability contract: SLIs/SLOs, error budgets, capacity planning, autoscaling (HPA/VPA/KEDA), and graceful degradation.
- Design and operate the observability stack - Prometheus, Grafana, OpenTelemetry, distributed tracing, and real-time alerting - for full visibility into system health.
- Drive DevOps and platform-engineering practices: IaC (Terraform), Helm, GitOps (ArgoCD/Flux), and CI/CD pipelines.
- Optimize for performance and cost - profiling, load testing, latency budgets, and cost-per-request.
- Participate in on-call rotations and lead incident response and root-cause analysis.
QualificationsRequired- 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); multi-cloud a strong plus.
- 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 (at-least-once vs. exactly-once), and message queues (Kafka/Pulsar/NATS/PubSub).
- Track record of building platforms from the ground up that other engineering teams successfully build on.
Bonus- 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).
Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.
