Industry: Software Development
Seniority Level: Mid–Senior
Experience: Minimum 7 Years
Job Type: Full-Time
Location: Remote (India)
Client Role

Role Overview

We are seeking a Head of AI Systems Engineering to lead the transformation of AI innovation into reliable, scalable, and production-grade systems. This role is accountable for the operational backbone of applied AI—ensuring models are not only built, but successfully deployed, monitored, optimized, and evolved in real-world environments.

Sitting at the intersection of AI research, engineering execution, and platform strategy, you will own how AI capabilities are delivered to customers and internal teams. This is a hands-on leadership role for someone driven by execution excellence, system reliability, and turning experimental models into business-critical infrastructure.

Key Responsibilities

AI Systems Ownership & Delivery

• Lead the transition of AI research outputs into stable, scalable, production-ready systems

• Own the full lifecycle of deployed models, from validation through decommissioning

• Define and enforce standards for model readiness, performance benchmarks, and operational handoff

• Ensure AI systems meet strict requirements for reliability, latency, scalability, and cost efficiency

Platform, Infrastructure & MLOps

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

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

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

• Design monitoring systems to detect model drift, accuracy degradation, latency issues, and cost anomalies

Inference & Performance Optimization

• Design and operate low-latency inference services with clearly defined SLAs

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

• Balance performance, quality, and infrastructure cost across diverse deployment environments

Leadership & Team Development

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

• Establish execution standards focused on reliability, speed, and continuous improvement

• Foster strong technical ownership and accountability across the team

Cross-Functional Collaboration & Strategy

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

• Manage and prioritize a pipeline of AI initiatives transitioning from experimentation to production

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

• 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 success deploying AI models at scale in real-world, customer-facing environments

• Strong foundation in machine learning across training, inference, and evaluation

• Hands-on expertise in MLOps, automation, and reliability engineering

• Deep experience with data pipelines, model monitoring, and observability frameworks

• Demonstrated leadership of senior engineers or applied AI teams

• Strong systems-thinking mindset with end-to-end ownership of complex initiatives

• Ability to operate effectively in fast-paced, ambiguous environments

• Excellent communication and stakeholder alignment skills

• High standards for accountability, technical judgment, and execution rigor

What Success Looks Like

• AI models operating reliably and predictably at scale in production

• Faster and smoother transitions from research to deployment

• High system uptime with controlled and optimized infrastructure costs

• Strong trust and collaboration across research, product, and engineering teams

• A mature, scalable foundation supporting future AI-driven products

Hiring Process Transparency

We may use artificial intelligence (AI) tools to support certain stages of the recruitment process, such as reviewing applications or analyzing responses. These tools assist our hiring team but do not replace human judgment. All final hiring decisions are made by people. For more information about how your data is processed, please contact us.