Platform Engineer
Builds and operates multi-tenant, cloud-native platform infrastructure for an agentic AI product. The role requires 5+ years of platform, infrastructure, or backend engineering experience, strong Golang and Python skills, Kubernetes expertise, and deep distributed-systems knowledge.
About the job
Responsibilities
- Design, own, and evolve scalable microservices architectures on Kubernetes across GCP, Azure, and AWS, including multi-tenant isolation with namespaces, network policies, per-tenant resource quotas, and RBAC.
- Build core platform and data-plane components in Golang and Python, including data ingestion, knowledge-base indexing and vector/graph search, application connectivity, workflow automation, and MLOps, against explicit latency and throughput SLOs.
- Own service-to-service communication, including gRPC/protobuf API contracts, service meshes, load balancing, retries, timeouts, and circuit breaking.
- Document architectural tradeoffs involving partitioning and sharding, consistency models, caching tiers, and build-versus-buy decisions.
- Define reliability contracts covering SLIs/SLOs, error budgets, capacity planning, autoscaling, and graceful degradation.
- Design and operate observability systems for system health visibility.
- Drive DevOps and platform-engineering practices, including infrastructure as code, Helm, GitOps, and CI/CD pipelines.
- Optimize performance and cost through profiling, load testing, latency budgets, and cost-per-request analysis.
- Participate in on-call rotations and lead incident response and root-cause analysis.
Requirements
- Bachelor's degree in Computer Science or a related field.
- 5+ years of experience in Platform, Infrastructure, or Backend Engineering.
- Strong computer science fundamentals, including data structures, algorithms, operating systems, and networking.
- Proficiency in Golang and Python.
- Production experience with Docker, Kubernetes, and microservices architecture.
- Hands-on experience with GCP, Azure, or AWS; multi-cloud experience is a strong plus.
- Strong database expertise, including query and read/write-path optimization, partitioning and sharding, replication, consistency models, NoSQL, and graph stores.
- Understanding of the CAP theorem and database internals.
- Distributed-systems experience with idempotency, backpressure, delivery semantics, and message queues.
- Track record of building platforms that other engineering teams successfully build on.
Nice-to-haves
- Experience operating systems at high scale, including high QPS and large data volumes.
- Experience with authentication and security, including Vault, mTLS, RBAC, OIDC/SAML, and network policies.
- Experience with vector databases such as pgvector, Pinecone, or Milvus.
- Experience with graph databases such as Neo4j or Neptune.
- Open-source contributions to infrastructure projects, such as Kubernetes operators.
Compensation and Benefits
- Compensation is determined by location, level, job-related knowledge, skills, and experience.
- Certain roles may be eligible for variable compensation, equity, and benefits.
Skills
Go, Python, Kubernetes, Docker, GCP, Azure, AWS, Microservices, gRPC, Istio, Linkerd, Terraform, Helm, Argo CD, Prometheus
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