About Us

Eliza is a technology services company and Advanced-tier OpenAI partner that's dedicated to helping organizations build and deploy cutting-edge AI solutions. From generative AI and custom LLM integrations to predictive analytics and intelligent automation, we work across industries to bring real-world AI applications to life. Our projects combine deep technical expertise with hands-on client collaboration to solve high-impact problems.

Job Summary

The Senior Forward Deployed Engineer, Cyber builds and deploys AI-enabled security solutions with customers. You will bring deep cybersecurity expertise and strong software engineering skills to work that spans discovery, architecture, integration, deployment, evaluation, and handover. You will own the engineering decisions that turn an ambiguous security problem into a useful, maintainable capability in the customer's environment.

This is a remote role with substantial autonomy and direct client contact. You will work closely with security practitioners, engineering teams, and delivery and commercial partners, making tradeoffs clear and keeping technical commitments credible. Experienced cyber engineers and senior technical leaders who want to remain hands-on are welcome.

Eliza is AI-native. You will use agentic coding tools in your engineering workflow (including OpenAI Daybreak), inspect and verify their output, and make practical decisions about where models help, where they fail, and where human judgment belongs. Client needs and technology change quickly; we value clear thinking, adaptability, and ownership through a validated outcome.

Key Responsibilities

1. Turn cyber needs into a technical plan

Lead technical discovery with customer security and engineering teams to understand workflows, systems, data, risks, and operating constraints.

Define a tractable problem, baseline, architecture, and acceptance criteria; explain what to build, defer, or escalate.

Align engineering work with measurable security outcomes, such as faster investigation or validated remediation, and make scope, dependencies, and tradeoffs visible.

2. Build and integrate AI-enabled security workflows

Write, review, and debug production software that connects models, APIs, telemetry, and security systems to support detection, investigation, and vulnerability remediation.

Choose tools and architecture that fit the engagement, balancing speed, reliability, maintainability, and cost.

Use AI coding tools effectively while retaining ownership of code quality, correctness, and security.

3. Deploy and operate in customer environments

Own deployment and troubleshooting with the appropriate customer owners, including configuration, access, CI/CD, monitoring, and integration failures.

Establish clear release, rollback, and operating procedures for the capability you deliver.

Resolve issues across system boundaries and surface technical or delivery risks early with a recommendation.

4. Evaluate quality and apply security judgment

Threat-model AI-enabled workflows and test realistic failure cases, including prompt injection, sensitive-data exposure, and unsafe tool actions, before treating a demonstration as production-ready.

Build practical controls across prompts, data flows, APIs, and cloud services, including least-privilege access, secrets protection, auditability, and human approval for consequential actions.

Verify that fixes address the original problem, document limitations, and involve specialist reviewers when needed.

5. Earn customer trust through technical delivery

Explain architecture, evidence, risks, and tradeoffs clearly to technical and nontechnical stakeholders.

Challenge requests that would undermine security or usefulness and offer practical alternatives.

Work with delivery and commercial partners on commitments and additional opportunities grounded in demonstrated customer value.

6. Leave maintainable capabilities behind

Produce clear documentation, interfaces, runbooks, and knowledge transfer so customer teams can operate and extend the work.