The Role
Own and evolve the core platform for model APIs and agentic products: build high-throughput, low-latency distributed services, data pipelines, SDKs, context retrieval, observability, and collaborate with ML/product teams to meet strict latency and reliability SLIs.
Summary Generated by Built In
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.
Skills Required
- 3+ years building and operating backend systems at scale
- 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
- Track record of designing APIs and frameworks adopted by other engineering teams
- Proficiency in at least one systems language (Go, Rust, Java, C++) or Python in a performance-sensitive context
- Comfortable across the stack: cloud infrastructure (AWS/GCP), containerized deployments (Kubernetes), CI/CD, and production oncall
- Experience with LLM serving, agentic orchestration patterns, or RAG pipelines
- Familiarity with 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) with correctness and auditability
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The Company
What We Do
We are transforming how stories are told, knowledge is learned, and insights are gathered
