Sr. Software Engineer, AI
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About the Role
Disclaimer: Please be advised that the most accurate and up-to-date information about our open roles—including job descriptions, compensation, and benefits—can only be guaranteed on our official job board. For the latest listings and details, please visit: https://job-boards.greenhouse.io/ninjatrader.
JOIN US ON OUR MISSION TO BECOME THE #1 RETAIL TRADING PLATFORM IN THE WORLD
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. How do we do it? We provide cutting-edge products and services that enhance the trading journey. Whether a seasoned pro or just starting out, NinjaTrader equips traders with award-winning software and brokerage services to navigate the world's leading financial markets with confidence.
Our growth story is nothing short of exhilarating. Since 2003, NinjaTrader has been dedicated to understanding and supporting traders on their journey toward trading triumph. Through those efforts, our user base has grown to over 2 million users and we have become the number one rated futures brokerage worldwide.
But we're not stopping there. We're constantly evolving, pushing boundaries, and modernizing the futures industry. Our commitment to innovation means users will always have access to dynamic tools, real-time support, and a community of like-minded traders.
So, why work at NinjaTrader? Here, you're not just part of a team; you're part of a movement. We empower employees to reach new heights in their careers by providing a dynamic culture focused on social connection, professional development, and employee recognition initiatives. Sounds too good to be true? Take it from our employees.
Join us as we redefine what's possible in trading, advocate for our customers, and continue our journey toward becoming the world's top retail-focused trading platform in the world.
What you'll do:
NinjaTrader is investing heavily in AI — not as a product feature, but as a force multiplier across the entire company. We’re hiring an internal, forward-deployed AI Engineer to accelerate the adoption of agentic AI across Engineering, Operations, Customer Experience, Data, Finance, and beyond. You’ll own AI infrastructure that serves every team in the company — we expect the work you build in your first year to save thousands of hours annually via 50+ new AI agents.
You’ll embed with internal teams, find the highest-leverage automation opportunities, and own them end-to-end: discovery, simplification, build, deployment, and adoption. You’ll scope a problem with a non-technical stakeholder in the morning and ship production infrastructure in the afternoon. You measure your work in hours unlocked and cycle time reduced — not stories closed.
In this role you will:
- Design and build multi-step agentic workflows in Python and TypeScript — 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
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