Staff Applied AI Engineer
Leads the design, governance, evaluation, and production delivery of AI systems for public-sector clients. The role requires 7+ years of engineering experience, production AI/ML ownership, expertise in regulated deployments, and the ability to establish technical standards and advise executive stakeholders.
About the job
Responsibilities
- Define standards for responsible AI, model governance, and production MLOps across the Global Public Sector team.
- Build or validate high-risk components of strategic AI systems and establish reusable standards.
- Architect AI systems designed to prevent systemic failure and lead resolution of severe incidents involving model safety or data integrity.
- Build reusable AI capabilities, including production-ready agent implementations, fine-tuned models, and evaluation methodologies.
- Advise on AI developments worth adopting and help set the technical roadmap.
- Act as a senior technical partner to client leadership on AI strategy.
- Coach Senior Applied AI Engineers, delegate technical-domain ownership, and develop systems and practices that enable multiple teams to deliver safer AI.
- Contribute to recruiting and represent the company's AI work externally.
Requirements
- 7+ years of engineering experience, including a multi-year track record owning AI/ML systems in production.
- Experience assessing training-data quality, selecting model-adaptation methods, evaluating fine-tuning results, and balancing serving cost, latency, and quality.
- Experience owning an AI-powered product end to end, including direct involvement in AI behavior.
- Track record of establishing durable standards, such as evaluation methodologies, MLOps practices, or architectural patterns.
- Comfort working with executive-level clients and leadership and defending technical positions under pressure.
- Deep experience with regulated, sovereign, or on-premises AI deployment, including hallucination mitigation and auditability.
- Some UK assignments may require BPSS screening and SC or DV clearance; eligibility depends on the assignment and candidate circumstances.
Collaboration Scope
- Own production AI behavior, backend integration, and customer-specific evaluation.
- Collaborate with Senior Full-Stack Engineers responsible for user-facing applications and infrastructure, and ML Research Engineers responsible for novel agent architecture and cross-account benchmark methodology.
Skills
Artificial Intelligence, Machine Learning, MLOps, Model Governance, Responsible Ai, AI Agents, Model Fine-Tuning, Training Data Evaluation, Ai Evaluation, Hallucination Mitigation, On-Premises Deployment, Auditability, Backend Integration, Production Systems
Similar jobs
ML Engineering jobs
Payabli
Remote
Sets the technical direction for production machine learning across a payments platform, building and scaling models for risk, authorization, disputes, and forecasting. Requires 8+ years of ML engineering experience, including production model ownership and strong technical leadership.
Build and deploy embedding, sequence, and language-model representations for Reddit Ads, taking ML projects from requirements and experimentation through production. The role requires 5+ years of end-to-end industry ML experience, with expertise in NLP or computer vision and deep-learning frameworks.
Hudl
Barcelona, SpainBuild and operate edge MLOps infrastructure for smart-camera machine-learning systems, including model deployment, TensorRT compilation, fleet updates, telemetry, and reliability. The role requires production MLOps experience, embedded inference optimization, and strong collaboration with data-science and embedded-engineering teams.
Rollstack
United StatesBuild production AI capabilities for automated slide and document generation, working across LLM applications, data analysis, and content generation. The role requires 3+ years in machine learning and NLP, advanced Python, and experience with LLM frameworks and production systems.
OpenAI
London, United Kingdom
Build and deploy AI-powered products for digital-native customers, taking systems from experimentation through production and scale. The role requires strong Python skills, hands-on production engineering, systematic AI evaluation, and the ability to navigate reliability, security, governance, and customer impact.