Sr. Software Engineer, AI

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

What you'll need:

• 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 for:

• 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

Compensation:

The salary range for this role will be $125,000.00 - $175,000.00 USD. In addition, this position will also receive an annual target bonus of 12%. Bonus pay at NinjaTrader is based on individual performance (50%) as well as company/team performance (50%).

Salary and bonus earnings are only two components of the total compensation package offered by NinjaTrader. NinjaTrader offers a 401K plan through ADP under which the company will match up to 3.5% of employee contributions. Annual paid time off allowance accrues at a rate of 18 days per year (some positions may qualify for more) plus seven paid holidays.

Location:

This role is based in Chicago, IL. We are not open to remote candidates for this role

Hybrid:

For Chicago-based employees, we follow a hybrid work schedule: In-office Tuesday through Thursday, with remote work on Mondays and Fridays. In addition to these weekly remote days, we offer:

• 20 additional flex remote days annually
• 5 Company Wide Office-Optional weeks tied to major holidays

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

What you'll need:

• 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 for:

• 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

Compensation:

The salary range for this role will be $125,000.00 - $175,000.00 USD. In addition, this position will also receive an annual target bonus of 12%. Bonus pay at NinjaTrader is based on individual performance (50%) as well as company/team performance (50%).

Salary and bonus earnings are only two components of the total compensation package offered by NinjaTrader. NinjaTrader offers a 401K plan through ADP under which the company will match up to 3.5% of employee contributions. Annual paid time off allowance accrues at a rate of 18 days per year (some positions may qualify for more) plus seven paid holidays.

Location:

This role is based in Chicago, IL. We are not open to remote candidates for this role

Hybrid:

For Chicago-based employees, we follow a hybrid work schedule: In-office Tuesday through Thursday, with remote work on Mondays and Fridays. In addition to these weekly remote days, we offer:

• 20 additional flex remote days annually
• 5 Company Wide Office-Optional weeks tied to major holidays