Summer Internship AI Engineer

Qualifications

• Implement only what is allowed and under review—no autonomous production changes without platform sign-off

• Pursuing a BS or MS in Computer Science, Computer Engineering, Information Systems, or a related field; expected graduation Summer 2026 or 2027

• 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)

• 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, Open Telemetry, CI/CD, Terraform/Ansible, or cloud (AWS/GCP/Azure).

Compensation range: $30.00-$35.00/hr DV is not accepting unsolicited resumes from search firms

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Benefits

• Compensation: $36k - $45k estimated

• Compensation: $36k - $45k estimated

Responsibilities

• Remote Generative assistant for alert response

• Learn our observability stack and what data exists today e.g. Prometheus, Grafana, Loki, Tempo, Alert manager, Open Telemetry

• 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

• Broader internal Q&AExplore 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

• 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:

• 10 more items(s)

More job highlights

Job description

Position: 2026 Summer Internship AI Engineer

Compensation: $36k - $45k estimated

Location:

Remote Generative assistant for alert response

Learn our observability stack and what data exists today e.g.

Prometheus, Grafana, Loki, Tempo, Alert manager, Open Telemetry.

Prototype a generative agent that uses approved observability sources to propose structured mitigation suggestions for alerts (hypot...hesis, 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&AExplore 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 graduation Summer 2026 or 2027.

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 >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, Open Telemetry, CI/CD, Terraform/Ansible, or cloud (AWS/GCP/Azure).

Compensation range: $30.00-$35.00/hr 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.

Apply Now:

Compensation: $36k - $45k estimated

Location:

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