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 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.

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
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

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