AI Engineer, Trust & Evaluation

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Equinix is the world’s digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet.

A place where tech thinkers and future builders turn bold ideas into breakthrough experiences, we welcome your unique perspective.Help us challenge assumptions, uncover bias, and remove barriers—because progress starts with fresh ideas. You’ll find belonging, purpose, and a team that welcomes you—because when you feel valued, you’re empowered to do your best work.

Job Summary

Designs, develops and deploys the evaluation and trust capabilities of Equinix's GenAI platform. Builds platform features that let teams evaluate their agents at scale: golden datasets, synthetic test data, scoring with LLM-as-judge, and monitoring of agent quality in production. Builds and tests the trust controls that agents run on, including agent identity, access, guardrails and data boundaries. Traces agent failures to their root cause and fixes them in the platform. Works in a team with senior engineers and with product, platform and security partners to turn defined problems into robust, secure, observable and performant solutions.

Knowledge and Application

Applies solid knowledge of software engineering practice and of LLM and agent concepts (prompting, retrieval, tool and function calling, orchestration, MCP). Understands evaluation methods (code-based checks, LLM-as-judge, human review, golden datasets, regression suites) and evaluation statistics (sampling, variance across runs, agreement between reviewers). Understands AI security risks such as prompt injection and data leakage, and identity concepts such as OAuth and per-user access. Follows established standards and suggests improvements.

Complexity and Problem Solving

Works on problems of defined scope where analysis requires evaluation of identifiable factors. Traces an agent failure to its cause (retrieval, tool call, prompt, model, data or infrastructure) using logs and traces, and follows it until it is fixed. Judges whether a metric reflects real quality. Escalates unusual or high-impact issues. Work is reviewed at defined checkpoints.

Collaboration and Interaction

Works with AI engineers, product managers, forward-deployed engineers, platform and security teams. Turns the needs of agent builders into platform features. Explains results, defects and risks to partners who are not familiar with the subject. Asks for guidance early and shares knowledge with the team.

Responsibilities

Evaluation platform (primary focus)

• Build platform features for evaluation: dataset creation and versioning, golden datasets, synthetic test data and reusable metric templates
• Build scoring for agents: code-based checks, LLM-as-judge, tool-call accuracy and grounding checks
• Build human-review features that compare judge scores against human reviewers, and recalibrate when agreement is low
• Run evaluations at scale: before release, on every change to a prompt, model or tool, and on agents in production
• Build evaluation playbooks so agent builders can evaluate their agents without specialist help
• Keep results traceable to the agent, model, prompt and dataset versions tested, with a record of who approved each release

Trust and safety

• Build and test the platform's trust controls: agent identity, per-user access to connected systems, tool permissions, guardrails and data boundaries
• Run adversarial tests and red-teaming (prompt injection, out-of-scope requests, data-boundary tests), and turn findings into fixes and regression tests
• Work with security and governance teams to turn policy requirements into automated checks

Root-cause analysis and quality

• Investigate agent failures and performance problems end to end, and follow them until the cause is fixed
• Build test and performance tooling: end-to-end agent tests, load and latency tests, and failure diagnostics
• Turn production failures and user feedback into regression tests, so the same defect does not return

Quality and responsible practice

• Write tested, reviewed and documented code, and contribute to shared libraries and tooling
• Test and build to security, data-privacy and responsible-AI requirements

Qualifications

Required

• 1–4 years in AI/ML or software engineering (internships and applied research count)
• Bachelor's degree in computer science, engineering, data science, mathematics or a related field, or equivalent experience
• Proficiency in Python, with solid habits: version control, testing, code review. Experience building backend services or platform features
• Hands-on experience building, not only using, evaluations or test systems for ML or LLM applications, including reading failures from logs or traces
• Working knowledge of LLM applications (prompting, retrieval, agents)
• Working knowledge of evaluation statistics (sampling, confidence intervals, variance across runs) and of measuring agreement between reviewers
• Ability to write SQL and analyze results
• Clear written and verbal communication

Nice to have

• Experience building evaluation or observability features used by many teams or at scale
• Experience with LLM evaluation and observability tools, and tracing standards such as OpenTelemetry
• Experience with red-teaming or adversarial testing of LLM systems
• Experience with identity and access (OAuth 2.0, OIDC, delegated access)
• Experience with performance, load or test engineering
• Experience with agent frameworks, MCP or A2A
• Experience with Google Cloud (Vertex AI, GKE) and Kubernetes
• Familiarity with AI risk frameworks (NIST AI RMF, ISO 42001)
• MS in a related field

What success looks like

• Platform features ship on schedule, with tests and documentation
• Teams can evaluate their agents on the platform without specialist help
• Every agent release carries an evaluation record with criteria, results, open defects and approver
• Judge scores agree with human reviewers, and teams use them to make release decisions
• Trust controls are enforced by the platform and covered by automated tests
• Defects found in evaluation become regression tests, and the same defect does not return
• The engineer takes on larger, less defined work each quarter with decreasing guidance

The targeted pay range for this position in the following location is / locations are:

Canada - Toronto Office TRO : 102,000 - 152,000 CAD / Annual

Our pay ranges reflect the minimum and maximum target for new hire pay for the full-time position determined by role, level, and location.The pay range shown is based on our compensation structure in place at the time of posting and may be updated periodically based on business needs. Individual pay is based on additional factors including job-related skills, experience, and relevant education and/or training.

The targeted pay range listed reflects the base pay only and does not include bonus, equity, or benefits. Employees are eligible for bonus, and equity may be offered depending on the position.

Equinix Benefits

As an employee, you become important to Equinix’s success. We ensure all your benefits are in line with our core values: competitive, inclusive, sustainable, connected and efficient. We keep them competitive within the current marketplace to ensure we’re providing you with the best package possible. So, wherever you are in your career and life, you’ll be able to enhance your experience and bring your whole self to work.

Employee Assistance Program: An Employee Assistance program is available to all employees.

Canada Core Benefits: - Insurance: You may enroll in healthcare coverage that is designed to complement the provincial healthcare system, along with life, disability and optional benefit plans that are designed for you and your eligible family members. - Retirement: You may also enroll in Equinix-sponsored retirement or savings plans: Defined Contribution Pension Plan (DCPP), Group Retirement Savings Plan (RRSP) and Tax-Free Savings Plan (TSFA). - Vacation and Paid Holidays: Equinix offers both vacation and personal time, along with various paid holidays for you to rest and recharge. Eligibility requirements apply to some benefits. Benefits are subject to specific plan or program terms, and to change at Equinix discretion.

Equinix is committed to ensuring that our employment process is open to all individuals, including those with a disability. If you are a qualified candidate and need assistance or an accommodation, please let us know by completing this form.

Equinix is an Equal Employment Opportunity and, in the U.S., an Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to unlawful consideration of race, color, religion, creed, national or ethnic origin, ancestry, place of birth, citizenship, sex, pregnancy / childbirth or related medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law.

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This posting is a new position within our organization.