About NinjaTrader
Welcome to the dynamic world of NinjaTrader! As an industry-leading trading platform and futures broker, we're empowering traders to take control of their financial destiny. We provide cutting-edge products and services that enhance the trading journey, equipping traders with award-winning software and brokerage services to navigate the world's leading financial markets with confidence. Since 2003, NinjaTrader has grown to over 2 million users and has become the number one rated futures brokerage worldwide. We are constantly evolving, pushing boundaries, and modernizing the futures industry, committed to innovation, dynamic tools, real-time support, and a community of like-minded traders.
About the Role
NinjaTrader is investing heavily in AI as a force multiplier across the entire company. We are hiring an internal, forward-deployed AI Engineer to accelerate the adoption of agentic AI across Engineering, Operations, Customer Experience, Data, Finance, and beyond. You will own AI infrastructure that serves every team in the company, with the expectation that your work in the first year will save thousands of hours annually via 50+ new AI agents.
You will embed with internal teams, identify high-leverage automation opportunities, and own them end-to-end: discovery, simplification, build, deployment, and adoption. You will scope a problem with a non-technical stakeholder in the morning and ship production infrastructure in the afternoon, measuring your work in hours unlocked and cycle time reduced—not stories closed.
Responsibilities
- Design and build multi-step agentic workflows in Python and TypeScript—including planning loops, tool dispatch, error recovery, and explicit human-in-the-loop checkpoints for high-stakes decisions.
- 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—embedding generation, vector/hybrid search, knowledge base ingestion—and apply judgment about when retrieval actually helps versus adds noise.
- Own eval discipline end-to-end: define offline eval sets, run A/B experiments on model changes, build regression suites, and articulate “good enough” exit criteria using LangSmith, Braintrust, or equivalent.
- Drive cost and latency optimization—token budgets, model tier selection (Haiku / Sonnet / Opus and GPT equivalents), and caching strategies that hold up at scale.
- Build MCP servers and function-calling connectors that give agents reliable, schema-governed access to internal tools, APIs, and data sources—Jira, CRM, Slack, internal services, and more.
- Implement and maintain production integrations using REST, GraphQL, webhooks, and event-driven patterns (queues, Pub/Sub) with proper idempotency, retry logic, and backfill support.
- Wire up OAuth/SAML authentication flows (Okta in particular) for secure agent-to-service access across internal and third-party systems.
- Own cloud infrastructure for AI workloads on GCP using Terraform, GKE/Cloud Run, and secrets management—with logging, metrics, and alerting from day one.
- Build data pipelines that feed AI systems: strong 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 where agentic automation can have the highest leverage—then build it.
- Create reusable libraries, SDKs, and internal tooling so teams can extend AI capabilities without starting from scratch.
- Act as a technical advisor and embedded engineer, translating ambiguous business problems into well-scoped AI systems with clear success metrics.
- Instrument and monitor deployed agents in production—you’re on-call for what you ship, and you treat 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—agents, structured outputs, tool use, RAG, evals, batch processing—shipped, not demoed.
- Forward-deployed instinct: engineering, developer relations, or solutions engineering experience.
- Strong evaluation discipline with the 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 the judgment to determine 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.
Bonus Points
- Trading industry, fintech, or capital markets experience.
- Futures trading knowledge.
- Experience with LangChain, LlamaIndex, or similar orchestration frameworks.
- Familiarity with observability tooling such as OpenTelemetry, Prometheus, and Grafana.
- Contributions to open-source AI or developer tooling projects.