AI Engineer

CONTEXT

Our client, a major player in its sector, is building a center of excellence where artificial intelligence and cybersecurity are not separate silos but two sides of the same trust equation. The organization operates at a scale where every model, every data flow, and every automation can influence millions of interactions, so the mandate is to move beyond demos and experiments into robust, production-grade AI. The team is looking for an engineer who can sit between business ambition and technical reality, translating high-level needs into architectures, pipelines, and applications that are secure by design and ready for real-world pressure.

ACTIVITIES

• Partner with product, security, and data stakeholders to frame business problems as concrete AI use cases with measurable outcomes.
• Design and build end-to-end AI applications, from data ingestion and feature preparation to model training, evaluation, deployment, and monitoring.
• Integrate AI services and custom models into existing platforms while respecting cybersecurity, privacy, and governance requirements.
• Automate model lifecycle workflows, including versioning, testing, release management, and observability.
• Prototype quickly, then harden promising solutions into scalable, maintainable production systems.
• Document architectural decisions and communicate trade-offs clearly to technical and non-technical audiences.
• Contribute to reusable components, standards, and best practices for the broader AI engineering community.

EXPECTED ACHIEVEMENTS

• Deliver production-ready AI applications that move from proof of concept to measurable business impact within clear timelines.
• Reduce the gap between experimentation and industrialization by establishing reproducible, observable, and secure deployment patterns.
• Strengthen the organization's ability to innovate responsibly, with AI solutions that pass security, compliance, and performance scrutiny.
• Create a durable foundation of shared engineering practices that other teams can reuse to accelerate future AI initiatives.

ENVIRONMENT

The role sits at the intersection of applied machine learning, software engineering, and cloud-native delivery. Expect to work with Python, modern ML libraries, API development, containerization, CI/CD pipelines, cloud AI services, monitoring tools, and secure data handling practices. The methodological backdrop includes agile delivery, MLOps principles, architecture reviews, threat-aware design, and close collaboration with cybersecurity specialists. The culture prizes curiosity, pragmatism, clean interfaces, and the ability to explain complex choices simply.