Head of AI Systems Engineering

This role is for one of our clients Industry: Software Development Seniority level: Mid-Senior level Min Experience: 7 years Location: Remote (India) JobType: full-time 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 AIensuring 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. Key Responsibilities: AI Systems Ownership & Delivery 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 Platform, Infrastructure & MLOps 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 Inference & Performance Optimization 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 Leadership & Team Development 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 Cross-Functional Collaboration & Strategy 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 What You Bring: 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 Robust systems-thinking mindset with the ability to own complex technical initiatives end-to-end Comfort operating in environments with ambiguity, rapid iteration, and high expectations Excellent communication skills and the ability to align diverse stakeholders A strong sense of ownership, accountability, and technical judgment What Success Looks Like: 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 We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. .