AI Engineer Intern - Summer 2027
AI in this role
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.
How we rate this
AI Engineer Intern - Summer 2027 at DV Trading rates 87 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: RAG. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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