Leads the Agentic Runtime team building low-latency, secure foundation for AI agents, handling orchestration, tool calling, model routing, and observability. Requires 8+ years in distributed systems, 1+ years management, and expertise in cloud-native services and LLMs.
250k – 300k/yr
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About the role
Responsibilities
Own impactful runtime problems end-to-end — from architecture and design to production launch and ongoing reliability.
Build and evolve core services for session lifecycle, streaming responses (e.g., gRPC/WebSockets), structured tool execution, memory/state, and policy/guardrails.
Design for performance, correctness, and cost: reduce p50/p95 latency, improve tail behavior, and optimize token/tool budgets.
Integrate with leading LLM providers (e.g., OpenAI, Anthropic, Google Gemini) and internal evaluation frameworks to improve quality and predictability.
Harden the platform with fault isolation, retries, timeouts, circuit-breaking, backpressure, and graceful degradation.
Instrument deep observability (tracing, metrics, logs) and create playbooks/SLOs for high availability and on-call excellence.
Collaborate closely with product, quality, and application teams to prioritize the most impactful roadmap investments.
Requirements
8+ years of software engineering experience building production distributed systems or cloud-native applications.
1+ years of engineering management experience.
BS/BA in Computer Science or related field, or equivalent practical experience.
Strong coding skills in at least one of: Python, Go, Java, or C++, with a focus on reliability, performance, and tests.
Product-minded: prioritize customer impact, clear SLAs/SLOs, and pragmatic iteration.
Ownership-driven with a positive, proactive attitude; comfortable leading projects or learning from battle-tested engineers.
Experience operating services on Kubernetes and at least one major cloud (e.g., GCP, AWS, or Azure).
Familiarity with event/streaming systems (e.g., Pub/Sub, Kafka), caching (e.g., Redis), and data stores for low-latency paths.
Practical understanding of LLM/agents building blocks: tool/function calling, structured outputs, streaming, and model selection/routing.
Strong observability and debugging skills: tracing (e.g., OpenTelemetry), metrics, dashboards, and production forensics.
Nice-to-Haves
Background in one or more areas: policy/guardrails, multi-tenant isolation, rate-limiting, concurrency control, cost optimization.
Compensation & Benefits
Base salary range: $250,000 - $300,000 annually.
Comprehensive benefits: Medical, Vision, Dental, generous time-off, 401k, home office stipend, education and wellness stipends, company events, daily lunches.
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