Summary
β¨ AIβGenerated
A senior systems engineering role focused on building reliable infrastructure for AI-powered applications. Responsibilities include backend platforms, data processing, runtime systems, and scalable service architecture.
Highlights
Develop core AI infrastructure powering advanced applications with ownership over scalable systems and challenging engineering problems.
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 β API serving, state management, data pipelines, context retrieval, and execution runtime β 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 workflowsDesign and operate high-throughput, low-latency distributed services that back our model API products β including request routing, load management, rate limiting, and multi-tenant isolationBuild and maintain downstream data pipelines (ETL/ELT) for API logs, usage analytics, and billing β ensuring data correctness, freshness, and queryability at scaleDevelop production-grade internal SDKs and libraries with clean APIs, strong type safety, and clear contracts that product teams can build on confidentlyArchitect context and memory systems for conversational workloads β low-latency retrieval, caching, and integration with vector stores and retrieval pipelinesInstrument end-to-end observability: define SLIs/SLOs, build structured logging and tracing, and drive reliability improvements across the platformCollaborate 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 β you've owned services that other teams depend on in productionStrong distributed systems fundamentals: concurrency, fault tolerance, consistency tradeoffs, capacity planningHands-on experience with data pipeline infrastructure (Kafka/Kinesis, Spark/Flink, Airflow, or similar) for log processing, analytics, or ETL workloadsTrack record of designing APIs and frameworks adopted by other engineering teams β you care about developer experience and long-term maintainabilityProficiency in at least one systems language (Go, Rust, Java, C++) or Python in a performance-sensitive contextComfortable 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 pipelinesFamiliarity 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
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information.
These tools assist our recruitment team but do not replace human judgment.
Final hiring decisions are ultimately made by humans.
If you would like more information about how your data is processed, please contact us.