AI Automation Engineer

ABOUT THE ROLE We are looking for a highly motivated AI Automation Engineer to join our team. In this role, you will be the primary engineer building and maintaining our technical interview pipeline - from the question bank to AI-assisted scoring to full workflow automation - working alongside the team to grow this role into a broader automation function over time. This isn't a "call an API and ship it" job: you'll be designing the systems that add/calibrate interview questions and evaluate candidate answers, which means understanding how to prompt and structure AI models for consistency, how to measure and validate output quality against ground truth, and how to catch and correct the ways these systems get things wrong. This is a role we expect to grow - the shape of that growth is still being defined.

KEY RESPONSIBILITIES Assessment Repository Management

• Add, revise, and retire technical interview questions across roles/levels/skill areas
• Maintain question metadata (difficulty, topic tags, expected answer criteria, role relevance)
• Create variant/randomized versions of questions to reduce answer-sharing risk
• Build validation checks (automated and manual) to catch incorrect, ambiguous, or miscalibrated questions before they enter the repository
• Build automation to update problem sets and recalculate average difficulty ratings as new results come in
• Review and update questions periodically to prevent leakage/staleness and keep content aligned with actual job requirements

Scoring & Answer Evaluation

• Architect scoring workflows that evaluate and compare candidate answers against reference answers/rubrics, grounded in a clear methodology for what "correct" and "well-scored" mean
• Calibrate and benchmark scoring prompts/models against human-graded samples, measuring accuracy and identifying systematic errors or bias
• Understand and account for model failure modes (inconsistency, hallucination, prompt sensitivity) and design safeguards around them
• Flag edge cases or low-confidence scores for human review rather than fully automating high-stakes decisions

Process Automation

• Automate the end-to-end workflow: question selection -> test delivery -> answer collection -> scoring -> reporting
• Help transition our source code and repositories to an internal Git platform
• Track test results to help establish scoring benchmarks over time

Quality & Fairness

• Track scoring consistency and accuracy over time; report on pipeline health
• Watch for and mitigate AI evaluation bias across candidate demographics or answer styles
• Document the process, prompts, and scoring logic for auditability

Growth & Scope

• Apply the same automation and AI-integration skills developed here to other internal workflows as the role expands
• Partner with engineering teams to identify manual, repetitive processes that are good candidates for automation
• Take on additional internal tooling and infrastructure projects as capacity and scope grow

REQUIRED QUALIFICATIONS

• Bachelor's degree in Computer Science or related field, or equivalent practical experience
• Solid programming fundamentals (Python, JavaScript/TypeScript, or similar)
• Comfort working with APIs, including LLM/AI APIs (OpenAI, Anthropic, etc.)
• Demonstrated experience designing and evaluating prompts/pipelines for LLM-based tools (not just using AI products, but building with them)
• Proficient with Git and a hosted platform (GitHub or GitLab) - branching, PRs/MRs, code review, CI basics
• Basic understanding of databases (SQL or similar)
• Strong attention to detail and technical writing ability
• Ability to work independently on processes that are still being defined, and adapt as scope evolves

PREFERRED QUALIFICATIONS

• Familiarity with test/assessment platforms or ATS integrations
• Exposure to scripting automation (e.g., workflow tools, cron jobs, CI-style pipelines)
• Interest or coursework in fairness/bias in ML systems
• Prior experience conducting or coordinating technical interviews (not required, but a plus)

Fortinet offers employees a variety of benefits, including medical, dental, vision, life and disability insurance, 401(k), 11 paid holidays, vacation time, and sick time as well as a comprehensive leave program. Wage ranges are based on various factors including the labor market, job type, and job level. Earnings for this position are expected to be $110,000 - $170,000Exact salary offers will be determined by factors such as the candidate's subject knowledge, skill level, qualifications, experience, and geographic location. All roles are eligible to participate in the Fortinet equity program, Bonus eligibility is reviewed at time of hire and annually at the Company’s discretion

Fortinet makes possible a digital world that we can always trust through its mission to protect people, devices, and data everywhere. This is why the world’s largest enterprises, service providers, and government organizations choose Fortinet to securely accelerate their digital journey. The Fortinet Security Fabric platform delivers broad, integrated, and automated protections across the entire digital attack surface, securing critical devices, data, applications, and connections from the data center to the cloud to the home office. Ranking #1 in the most security appliances shipped worldwide, more than 615,000 customers trust Fortinet to protect their businesses. And the Fortinet NSE Training Institute, an initiative of Fortinet’s Training Advancement Agenda (TAA), provides one of the largest and broadest training programs in the industry to make cyber training and new career opportunities available to everyone. We are looking for a highly motivated AI Automation Engineer to join our team. In this role, you will be the primary engineer building and maintaining our technical interview pipeline - from the question bank to AI-assisted scoring to full workflow automation - working alongside the team to grow this role into a broader automation function over time. This isn't a "call an API and ship it" job: you'll be designing the systems that add/calibrate interview questions and evaluate candidate answers, which means understanding how to prompt and structure AI models for consistency, how to measure and validate output quality against ground truth, and how to catch and correct the ways these systems get things wrong. This is a role we expect to grow - the shape of that growth is still being defined.