Senior AI Engineer (Agent OS Platform)
About The Position
ServiceTitan runs the businesses behind the trades: jobs, trucks, technicians, equipment, contracts, payments, warranties, compliance obligations, and customer history. That operational context is our advantage. We are building Agent OS to turn that context into safe, observable, production-grade agent work. Agent OS is the shared runtime, context, memory, action, trust, and evaluation layer behind role-specific AI experiences across Atlas, office, field, voice, mobile, and future product surfaces. This is not a collection of chatbots. It is the platform that lets agents help contractors run their businesses with the right evidence, permissions, approvals, and audit trails. You will help build the core engineering primitives behind that platform: agent runtime, typed tools, context and memory assembly, trust and approval flows, evaluation infrastructure, and production observability. You are not building one agent for one product surface. You are building the platform that product teams use to build many agents safely. You will work on a small, senior AI platform team and partner closely with Product, Architecture, Security, Data Platform, Atlas, and domain engineering teams.
Requirements
- 5+ years of production software engineering experience.
- Strong hands-on coding ability in Python, Java, C#, or another backend language. Python experience is strongly preferred.
- Experience building AI, ML, data, platform, infrastructure, workflow, automation, or developer-platform systems in production.
- Practical understanding of modern LLM application architecture: model gateways, prompt and context assembly, retrieval, tool calling, structured outputs, memory, agent workflows, and human approval patterns.
- Experience with distributed systems, event-driven systems, async workflows, queues, durable execution, or message-driven architectures.
- Strong production-safety instincts for non-deterministic systems: typed contracts, scoped permissions, precondition checks, idempotency, audit trails, rollback, and monitoring.
- Experience designing or operating evaluation systems: behavioral evals, regression suites, scenario tests, trajectory review, simulation, online metrics, or production monitoring.
- Strong data and context instincts: SQL, unstructured data, vector search, metadata, provenance, freshness, source authority, and privacy boundaries.
- Experience with databases, warehouses, or search systems such as PostgreSQL, SQL Server, Snowflake, BigQuery, Elasticsearch, or vector stores.
- Experience building services on public cloud infrastructure such as Azure, AWS, or GCP.
- Good engineering judgment across APIs, reliability, security, observability, and multi-tenant SaaS constraints.
Nice To Haves
- Experience building or operating agent runtimes, workflow engines, model gateways, ML platforms, evaluation platforms, developer platforms, or internal control planes.
- Experience with LangGraph, LangChain, LlamaIndex, Semantic Kernel, OpenAI Agents SDK, Anthropic tooling, or similar frameworks.
- Experience with MCP, A2A, tool protocols, agent interoperability, or agent-commerce patterns.
- Experience with Kubernetes, Docker, serverless platforms, or cloud-native infrastructure.
- Experience with compliance-sensitive workflows, approval-gated automation, audit trails, policy engines, or governed writes to systems of record.
- Experience in SaaS, vertical software, fintech, ERP, CRM, marketplace, field service, or other domains where software decisions affect real business operations.
- Experience with graph-based data models, knowledge graphs, entity resolution, or cross-domain operational context systems.
Responsibilities
- Design and implement core Agent OS platform services.
- Write production code and review implementation details from other engineers.
- Build reliable APIs, workflows, tools, and services for agent execution.
- Inspect traces, debug failures, and improve production behavior.
- Design evaluation scenarios and regression suites for agent workflows.
- Work through real agent failure modes: stale context, wrong tool calls, missing permissions, unsafe actions, poor retrieval, latency spikes, and cost regressions.
- Partner with domain teams to turn agent use cases into reusable platform patterns.
- Help define platform contracts for tools, actions, approvals, context, memory, evidence, and evaluation.
- Contribute to technical direction while staying grounded in what can ship quickly and safely.
- Communicate clearly with engineers, product managers, architects, security partners, and engineering leadership.
Benefits
- Flexible time off
- Learning and development opportunities
- Comprehensive onboarding program
- Leadership training
- Peer-nominated awards
- Company-paid medical, dental, and vision (with 100% employer paid options and 90% coverage for dependents)
- FSA and HSA
- 401k match
- Telehealth options including memberships to One Medical
- Parental leave and support
- Up to $20k in fertility services (i.e. IUI and IVF), surrogacy, and adoption reimbursement
- On demand maternity support through Maven Maternity
- Free breast milk shipping through Maven Milk
- Pet insurance
- Legal advisory services
- Financial planning tools
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
