Head of AI Systems Engineering

We are looking for a Head of AI Systems Engineering to lead the transformation of AI innovation into dependable, production-grade systems. This role owns the operational backbone of applied AI—ensuring models are not just built, but successfully deployed, scaled, monitored, and evolved in real-world environments.

You will sit at the convergence of research, engineering, and platform strategy, taking accountability for how AI capabilities are delivered to customers and internal users. This is a hands-on leadership role for someone who thrives on execution rigor, system reliability, and turning experimental models into business-critical infrastructure.

Lead the conversion of AI research outputs into stable, scalable, and production-ready systems

Own the full lifecycle of deployed models, from initial validation to sunset and replacement

Define clear standards for model readiness, performance thresholds, and operational handoff

Ensure production AI systems meet reliability, latency, cost, and scalability expectations

Architect and operate AI platforms supporting both large-scale training and real-time inference

Build and maintain end-to-end ML pipelines covering data ingestion, training, evaluation, deployment, and monitoring

Implement robust CI/CD workflows for models, including versioning, rollback, testing, and observability

Design monitoring systems to track model health, drift, accuracy, latency, and cost efficiency

Design low-latency inference services with clearly defined SLAs

Apply model optimization techniques such as compression, quantization, distillation, or hardware acceleration

Balance performance, quality, and cost across different deployment environments

Lead and grow a multidisciplinary team of ML engineers, MLOps specialists, and applied AI practitioners

Establish execution standards that prioritize reliability, speed, and continuous improvement

Mentor senior contributors and build strong technical ownership across the team

Act as the primary bridge between AI research, product, and engineering teams

Manage and prioritize a pipeline of AI initiatives moving from experimentation into production

Contribute to long-term AI platform strategy, architecture decisions, and roadmap planning

Partner with cloud and AI platform vendors to leverage advanced tooling and optimize infrastructure spend

6+ years of experience building and operating production-grade AI or ML systems

Proven track record of taking models from experimentation into large-scale, real-world deployment

Strong grounding in machine learning fundamentals across training, inference, and evaluation

Hands-on experience with MLOps practices, automation, and reliability engineering

Deep familiarity with data pipelines, model monitoring, and observability frameworks

Experience leading senior engineers or applied AI teams

Strong systems-thinking mindset with the ability to own complex technical initiatives end-to-end

Comfort operating in environments with ambiguity, fast iteration, and high expectations

Excellent communication skills and the ability to align diverse stakeholders

A strong sense of ownership, accountability, and technical judgment

AI models reliably operating at scale in production

Faster, smoother transitions from research to deployment

High system uptime, predictable performance, and controlled infrastructure costs

Strong trust from research, product, and engineering teams

A mature, scalable foundation for future AI-driven products