Job description

About Boson AI: At Boson AI, we are not just building AI solutions; we are pioneering the future of enterprise AI. Driven by a passion for cutting-edge AI research, particularly in the transformative areas of large language models and agentic systems, our mission is to tackle the most complex real-world problems for businesses and unlock significant value. We are a dynamic and collaborative team of researchers and engineers who thrive on pushing the boundaries of what's possible, dedicated to delivering high-quality, reliable products that seamlessly integrate into the fabric of enterprise workflows and set new industry standards. About the Role: Build and operate the core platform behind Boson's model APIs and agentic products. You'll own the infrastructure that every Boson agent runs on — API serving, state management, data pipelines, context retrieval, and execution runtime — and make it fast, reliable, and easy for product teams to build on. Responsibilities 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. Qualifications 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. Bonus point 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.

Requirements

Must-haves

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.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aiapimlgorustjavapythonawsgcpci/cdllmreact

Platform: lever
Effort: quick