Sr. Software Engineer, AI

NinjaTrader•Chicago, IL

6d•$125,000 - $175,000•Hybrid

About The Position

NinjaTrader is heavily investing 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. This role will own AI infrastructure that serves every team in the company, with the expectation that the work built in the first year will save thousands of hours annually via 50+ new AI agents. The engineer will embed with internal teams, identify high-leverage automation opportunities, and own them end-to-end: discovery, simplification, build, deployment, and adoption. The role involves scoping problems with non-technical stakeholders and shipping production infrastructure, measuring work in hours unlocked and cycle time reduced.

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

Nice To Haves

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

Responsibilities

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