About this role
- Own and evolve the core platform infrastructure: API serving layer, state management, policy enforcement engine, and execution runtime for agentic workflows.
- Design and operate high-throughput, low-latency distributed services that back our model API products — including request routing, load management, rate limiting, and multi-tenant isolation.
- Build and maintain downstream data pipelines (ETL/ELT) for API logs, usage analytics, and billing — ensuring data correctness, freshness, and queryability at scale.
- Develop production-grade internal SDKs and libraries with clean APIs, strong type safety, and clear contracts that product teams can build on confidently.
- Architect context and memory systems for conversational workloads — low-latency retrieval, caching, and integration with vector stores and retrieval pipelines.
- Instrument end-to-end observability: define SLIs/SLOs, build structured logging and tracing, and drive reliability improvements across the platform.
- Collaborate closely with ML and product teams to integrate model serving, voice runtime, and tooling infrastructure under tight latency and quality constraints.
- 3+ years building and operating backend systems at scale — you've owned services that other teams depend on in production.
- Strong distributed systems fundamentals: concurrency, fault tolerance, consistency tradeoffs, capacity planning.
- Hands-on experience with data pipeline infrastructure (Kafka/Kinesis, Spark/Flink, Airflow, or similar) for log processing, analytics, or ETL workloads.
- Track record of designing APIs and frameworks adopted by other engineering teams — you care about developer experience and long-term maintainability.
- Proficiency in at least one systems language (Go, Rust, Java, C++) or Python in a performance-sensitive context.
- Comfortable working across the stack: cloud infrastructure (AWS/GCP), containerized deployments (K8s), CI/CD, and production oncall.
- Experience with LLM serving, agentic orchestration patterns (ReAct, planner-executor), or RAG pipelines.
- Familiarity with emerging agent integration protocols (MCP, A2A) or orchestration frameworks (LangChain, LlamaIndex).
- Background in real-time media systems (audio/video streaming, low-latency signaling).
- Experience building high-stakes platform services (payments, identity, core data) where correctness and auditability are non-negotiable.
Tech stack
KafkaSparkAirflowRustJavaPython
Salary context
+50%
above median
Based on 17,000+ Engineering roles with disclosed salary ranges tracked on NewJob.
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