Staff AI Process Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff AI Process Engineer based in United States.

This is a high-impact engineering role focused on transforming complex, manual workflows into scalable, AI-enabled <systems.You> will uncover how expert-driven processes actually work, separating genuine judgment from habits, workarounds, and outdated tooling.From there, you will design and deploy modern solutions using LLMs, AI agents, automation, and conventional engineering where <appropriate.You> will own the journey from process discovery and knowledge extraction through production deployment and continuous improvement.A key focus will be building reliable agent workflows with evaluation, confidence thresholds, fallbacks, and human <oversight.You> will work closely with domain experts, earning trust while turning tacit knowledge into transparent and verifiable systems.The role offers an opportunity to shape how critical processes are modernized in a technically sophisticated and innovation-driven environment.

Accountabilities

Process discovery and knowledge extraction: Work directly with domain experts to observe real-world workflows, document tacit knowledge, identify decision points and edge cases, and distinguish genuine expertise from routine practices or outdated assumptions.

Process modeling and modernization: Translate informal, expert-driven processes into formal, structured models that can be analyzed, improved, and ultimately supported or automated through technology.

Legacy tooling transformation: Assess bespoke and legacy tools to understand their actual functionality, identify modernization opportunities, and design AI-native or conventional engineering replacements without disrupting ongoing operations.

AI agent architecture: Design and implement multi-step AI agent workflows that break complex tasks into verifiable stages, incorporating appropriate tool use, context management, evaluation frameworks, and failure or fallback strategies.

Evaluation and reliability: Establish methods for comparing AI-generated outcomes against expert baselines and introduce confidence-based escalation mechanisms so systems can recognize when human intervention is required.

Human-in-the-loop design: Define where human approval, review, or override is necessary, including escalation thresholds, audit trails, feedback loops, and calibration mechanisms that continuously improve system performance.

Knowledge transition: Help domain experts evolve from process executors into reviewers and teachers while preserving valuable institutional knowledge and maintaining meaningful engagement.

End-to-end delivery: Own initiatives from discovery through production deployment, balancing technical ambition with pragmatic delivery and ensuring solutions are scalable, transparent, auditable, and operationally sustainable.

Requirements

Education: Bachelor’s degree from an accredited university or college.

Professional experience: At least 5 years of experience in software or AI engineering, including a minimum of 2 years building LLM-based or AI agent-based systems.

AI engineering expertise: Demonstrated experience designing and deploying multi-step, tool-using AI agent workflows, including evaluation and orchestration.

Process expertise: Proven ability to extract tacit knowledge from subject-matter experts through process mining, cognitive task analysis, knowledge engineering, or comparable approaches.

Systems thinking: Strong ability to understand and model complex processes end-to-end before determining the appropriate technical solution.

Modernization experience: Track record of replacing or modernizing legacy systems while maintaining business continuity and minimizing operational disruption.

Technical judgment: Ability to determine when AI, LLM pipelines, vision models, structured extraction, or conventional software engineering are the right approaches for a given problem.

Domain knowledge: Experience in aerospace, aviation regulatory organizations, or other highly regulated or safety-critical environments is valued.

Specialized knowledge: Background in knowledge engineering, expert systems, decision-support systems, or comparable disciplines is advantageous.

Communication and influence: Diplomatic persistence and strong interpersonal skills, with the ability to build trust with experts and influence stakeholders through collaboration rather than authority.

Problem-solving mindset: Strong decomposition skills, healthy skepticism toward undocumented processes, and a pragmatic approach to delivering valuable solutions with appropriate human oversight.

Benefits

Competitive base salary: $152,000–$202,000 USD per year, with the actual offer determined by factors including experience, education, skills, and qualifications.

Performance incentives: Eligibility for an annual discretionary bonus based on a percentage of base salary or applicable compensation plan.

Remote work: Fully remote position within the United States.

Healthcare coverage: Medical, dental, vision, and prescription drug benefits.

Wellbeing support: Access to health coaching and a confidential Employee Assistance Program offering 24/7 assessment, counseling, and referral services.

Retirement benefits: 401(k) savings plan with matching contributions and employer retirement contributions, plus access to financial planning resources.

Additional programs: Tuition assistance, adoption assistance, paid parental leave, disability insurance, life insurance, and paid time off for vacation or illness.

Professional environment: Opportunities for professional development and challenging work at the intersection of AI, software engineering, process transformation, and complex operational environments.

Relocation: No relocation assistance is provided.