Sr. Software Engineer, AI

NinjaTrader is an industry-leading trading platform and futures broker dedicated to empowering traders. They are seeking a Senior Software Engineer specializing in AI to develop and implement AI solutions across various internal teams, aiming to enhance efficiency and productivity through automation.

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

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
• 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