Jobs › DV Trading

AI Engineer Intern - Summer 2027

DV Trading · Chicago, IL · $40.00–$50.00 an hour · Technology

Posted Sep 9, 2026 · We last checked this listing on Oct 6, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

Tell me about an AI or LLM project you built. What did not work the first time?

What they are checking: Hands-on experience and honesty about the trial and error that real AI work involves.

The internship focuses on internal tooling, not customer-facing models. Why does that interest you?

What they are checking: Whether they are motivated by practical engineering impact rather than only model training.

What would you do

One project is a generative assistant that proposes mitigation steps for alerts using approved observability sources. How would you start building it?

What they are checking: Whether they would learn the data and the alert workflow first instead of jumping straight to prompts.

You will report to the DevOps lead and partner with other teams. How would you get feedback from engineers who are busy?

What they are checking: Whether they seek out users early and make it easy for them to give input.

About the work

The assistant should trace its suggestions back to queries, dashboards or signals. Why does that matter, and how would you do it?

What they are checking: Understanding that engineers on call need to verify AI output, so grounding and citations matter.

What is retrieval-augmented generation, and when is it better than fine-tuning?

What they are checking: Basic grasp of how to give a model current, specific information without retraining it.

The observability stack includes Prometheus, Grafana, Loki, Tempo and Alertmanager. Which of these have you used, and what does each one hold?

What they are checking: Familiarity with the data sources the assistant would pull from.

How would you stop the assistant from suggesting an unsafe action on a production trading system?

What they are checking: Thinking about guardrails, read-only access and keeping a human in charge of real changes.

How would you measure whether the alert assistant is actually helping engineers?

What they are checking: Ability to define useful measures like time to resolve and how often suggestions were right.

The model gives a confident answer that is wrong. How would you design for that?

What they are checking: Awareness of hallucination and design choices like showing evidence and stating uncertainty.

Practice this interview out loud.

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The full job description

As published by DV Trading.

About Us : Founded 20 years ago and headquartered in Chicago, the  DV  Group of financial services firms has grown to more than 600 people operating throughout North America, Europe and Asia. Since spinning out of a large brokerage firm in 2016,  DV  Trading has rapidly scaled as an independent proprietary trading firm utilizing its own capital, trading strategies, and risk management methodologies to provide liquidity to worldwide financial markets and hedging opportunities to commodity producers and users. Now,  DV  group affiliates include two broker dealers, a cryptocurrency market making firm, and a bourgeoning investment adviser. Role Overview: This is a project-focused internship for an AI engineer embedded on the DevOps team. You will report to the DevOps lead and partner with internal technology teams. The work centers on internal, production-adjacent tooling—not training or shipping customer-facing ML models. Core Internship Projects: • Generative assistant for alert response • Learn our observability stack and what data exists today e.g. Prometheus, Grafana, Loki, Tempo, Alertmanager, OpenTelemetry. • Prototype a generative agent that uses approved observability sources to propose structured mitigation suggestions for alerts (hypothesis, checks, likely causes, safe next steps), with traceability back to queries, dashboards, or signals where possible. • Retrieval on internal data (RAG) • Build and iterate on RAG over permissioned internal data sources (e.g. runbooks, tickets, docs, system design, network design, postmortems) so suggestions and Q&A are grounded and citeable. • Work with teams to improve coverage and quality of that corpus (metadata, ownership, freshness). • Path toward agentic remediation (design + scoped implementation) • Outline how the system could execute approved remediations behind explicit guardrails and human approval. • Implement only what is allowed and under review—no autonomous production changes without platform sign-off. • Broader internal Q&A • Explore how additional internal, permissioned firm data can support natural language questions for engineers. • Across all phases, permissioning, auditing, logging, and cost controls are non-negotiable requirements, not stretch goals. Responsibilities: • Design and prototype agent workflows with tool use, policy boundaries, and human-in-the-loop where appropriate. • Collaborate with platform and service teams to make more observability and operational context available in a safe, governed way for agents. • Document experiments, limitations, evaluation approach, and safety assumptions; ship changes via Git (branches, merge requests, meaningful commits). Requirements: • Pursuing a BS or MS in Computer Science, Computer Engineering, Information Systems, or a related field • Expected to graduate by Summer 2028 • Hands-on experience using AI tools (e.g. LLM APIs, assistants, or coding agents) in real projects; preferably experience building an agent (tools, orchestration, or similar—not only prompt-only chat). • Experience with RAG (retrieval design, chunking, evaluation, grounding, or production-minded prototyping)—including applying it to real or simulated internal/knowledge-base style data, not only public tutorials. • Python for prototyping and integration; comfortable consuming and creating APIs and working with JSON; able to understand YAML for configs. • Git workflow: branches, merge requests, meaningful commit messages. • Strong judgment on data handling: no secrets in prompts/logs, minimize sensitive data, follow internal policies. Preferred Skills: • Linux fundamentals (shell, processes, logs, permissions, basic troubleshooting). • Networking basics: DNS, TCP/HTTP/S, ports, load balancing vs Ingress at a conceptual level. • Kubernetes fundamentals: debugging, pods, services, ingress Coursework or projects involving Kubernetes, Prometheus/Grafana, OpenTelemetry, CI/CD, Terraform/Ansible, or cloud (AWS/GCP/Azure). Compensation Range: $40.00-$50.00 per hour DV is not accepting unsolicited resumes from search firms. Only search firms with valid, written agreements with DV should submit resumes in response to DV’s posted positions. All resumes submitted by search firms to DV via e-mail, the Internet, personal delivery, facsimile, or any other method without a valid written agreement shall be deemed the sole property of DV, and no fee will be paid in the event the candidate is hired by DV. DV is proud to be an equal opportunity employer and committed to creating an inclusive environment for all employees.  

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