Architect, build, deploy, and operate production-grade AI agentic systems and workflows. Responsibilities include backend and full-stack development, agent orchestration, workflow state, retrieval, memory, approvals, failure recovery, data architecture, enterprise integrations, cloud infrastructure, observability, security, and scaling. The role establishes reusable engineering patterns and partners with users to deliver reliable AI products, while addressing production issues involving model behavior, permissions, latency, cost, and consistency.
Company Introduction
At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.
We are seeking a Senior Software Engineer, Applied AI to architect, build, ship, and operate production-grade AI and agentic systems across Bot Auto. This is an engineering-first role for a senior software engineer with proven experience building real AI agentic products or business workflows in production. You should be comfortable owning the system end to end across backend services, full-stack applications, databases, infrastructure, integrations, and modern AI/LLM engineering, while making sound decisions about where probabilistic AI should and should not be used.
Key Responsibilities- Own architecture and end-to-end implementation of production agentic products and workflows from problem definition through deployment and operation.
- Design boundaries between deterministic software, workflow engines, databases, source systems, and probabilistic agent reasoning.
- Build and review backend services, APIs, full-stack applications, and operator/user-facing tools supporting agentic workflows.
- Design agent orchestration, tool contracts, workflow state, memory, context, retrieval, approvals, escalation, retries, idempotency, rollback, and failure recovery.
- Design production data architecture, including relational schemas, state persistence, caching/search, event systems, and consistency behavior.
- Integrate AI systems with enterprise tools, internal APIs, data platforms, and operational systems while preserving permissions, auditability, and source-of-authority boundaries.
- Partner with infrastructure teams on cloud architecture, containers, CI/CD, queues, observability, tracing, security, scaling, latency, and cost.
- Establish reusable engineering patterns and reference implementations; work directly with users to convert high-value workflows into reliable products.
- 6+ years of relevant professional software engineering experience, or equivalent demonstrated experience building and shipping production systems.
- Demonstrated hands-on experience architecting, building, shipping, and operating production-grade AI agentic applications, products, or business workflows.
- Strong backend and full-stack engineering capability across services, APIs, databases, application state, and user-facing web applications.
- Strong programming skills in Python and at least one modern application language such as TypeScript/JavaScript, or Go.
- Strong understanding of distributed systems, databases, data modeling, transactions/consistency, event-driven architectures, and production application design.
- Strong working knowledge of cloud infrastructure, containers, CI/CD, observability, security, and production operations.
- Deep practical understanding of LLM/agent systems, including tool calling, orchestration, context, retrieval, memory, workflow state, human review, guardrails, and failure recovery.
- Experience owning real production issues such as nondeterministic failures, stale context, tool errors, duplicate actions, permission failures, model regressions, latency, and cost trade-offs.
- Experience with MCP, LangGraph, LangChain, LlamaIndex, or equivalent agent/tool orchestration frameworks.
- Experience with Temporal or another durable workflow engine for long-running, stateful workflows.
- Experience designing RAG, hybrid retrieval, vector search, graph/knowledge systems, or context-engineering architectures.
- Experience with eval-driven development, tracing, offline/online evaluation, model/prompt versioning, guardrails, and regression testing.
- Experience leading technical initiatives across teams; experience in autonomous systems, logistics, robotics, or other mission-critical domains is a plus.
Compensation
For candidates based in California, the anticipated annual base salary range for this position is $160,000–$220,000. The final base salary will be determined based on factors such as the candidate’s qualifications, relevant experience, demonstrated skills, position level, scope of responsibilities, and work location. This base salary range does not include any applicable bonus, equity, or benefits.
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Austin has a diverse and thriving tech ecosystem thanks to home-grown companies like Dell and major campuses for IBM, AMD and Apple. The state’s flagship university, the University of Texas at Austin, is known for its engineering school, and the city is known for its annual South by Southwest tech and media conference. Austin’s tech scene spans many verticals, but it’s particularly known for hardware, including semiconductors, as well as AI, biotechnology and cloud computing. And its food and music scene, low taxes and favorable climate has made the city a destination for tech workers from across the country.
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