About the Role
You'll be an internal, forward-deployed AI engineer accelerating the adoption of agentic AI across the company. You'll embed with internal teams, find the highest-leverage automation opportunities, and own them end-to-end from discovery through deployment and adoption. You'll scope a problem with a non-technical stakeholder in the morning and ship production infrastructure in the afternoon, measuring your impact in hours unlocked and cycle time reduced. You'll design multi-step agentic workflows, build RAG pipelines, own eval discipline, drive cost and latency optimization, build MCP servers and integrations, and own cloud infrastructure for AI workloads. You'll partner across teams to translate ambiguous business problems into well-scoped AI systems, and you'll monitor what you ship in production, treating reliability as a feature.
Requirements
- 5+ years of production software engineering experience, primarily in Python or TypeScript; Go is a plus
- Production LLM application experience with Anthropic or OpenAI SDKs, including agents, structured outputs, tool use, RAG, evals, and batch processing
- Forward-deployed instinct: engineering, developer relations, or solutions engineering experience
- Strong evaluation discipline with ability to define and defend exit criteria using LangSmith, Braintrust, or equivalent tools
- Experience building multi-step tool-using agents with planning, error recovery, and human-in-the-loop design in production environments
- Experience with RAG pipelines, embeddings, hybrid search, and judgment on when retrieval improves outcomes
- Experience building MCP servers, function-calling schemas, and sandboxed execution environments
- Strong understanding of token budgets, model tier trade-offs, and AI cost/latency optimization strategies
- Experience integrating REST APIs, GraphQL, webhooks, OAuth/SAML authentication (especially Okta), and event-driven architectures
- Cloud-native engineering experience with GCP or AWS, including Terraform, containers, secrets management, logging, metrics, and alerting
- Strong SQL and data engineering experience with modern warehouses, ETL/ELT pipelines, schema design, and data-quality monitoring
- Ability to work cross-functionally and translate ambiguous business problems into production-ready AI systems
- Strong communication skills with both technical and non-technical stakeholders
Responsibilities
- Design and build multi-step agentic workflows in Python and TypeScript, including planning loops, tool dispatch, error recovery, and human-in-the-loop checkpoints
- Develop production LLM applications on Anthropic and OpenAI SDKs, including prompt engineering, structured outputs, tool/function calling, prompt caching, and batch processing
- Build and maintain RAG pipelines, including embedding generation, vector/hybrid search, and knowledge base ingestion
- Own eval discipline end-to-end, defining offline eval sets, running A/B experiments, building regression suites, and articulating exit criteria using LangSmith, Braintrust, or equivalent
- Drive cost and latency optimization through token budgets, model tier selection, and caching strategies
- Build MCP servers and function-calling connectors for reliable, schema-governed access to internal tools, APIs, and data sources
- Implement and maintain production integrations using REST, GraphQL, webhooks, and event-driven patterns with idempotency, retry logic, and backfill support
- Wire up OAuth/SAML authentication flows, particularly Okta, for secure agent-to-service access
- Own cloud infrastructure for AI workloads on GCP using Terraform, GKE/Cloud Run, and secrets management with logging, metrics, and alerting
- Build data pipelines that feed AI systems, including SQL, Athena/BigQuery-class warehouses, ETL/ELT, schema design, and data-quality monitoring
- Partner with internal teams across Engineering, Operations, Customer Support, Data, and Finance to identify high-leverage automation opportunities
- Create reusable libraries, SDKs, and internal tooling so teams can extend AI capabilities
- Act as a technical advisor and embedded engineer translating ambiguous business problems into well-scoped AI systems
- Instrument and monitor deployed agents in production and remain on-call for what you ship
Benefits
- 401K plan through ADP with company match up to 3.5% of employee contributions
- Annual paid time off accruing at 18 days per year plus seven paid holidays
- 20 additional flex remote days annually
- 5 Company Wide Office-Optional weeks tied to major holidays
- Generous PTO
- 7 Paid Holidays Annually + 5 Conditional Holidays Annually
- 1 Service Day Annually
- Paid Parental Bonding Leave
- Health, Vision, Dental Coverage
- Life and Disability Insurance Covered 100% by NinjaTrader
- Hybrid work schedule with remote work on Mondays and Fridays