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Manager - Applied AI at Weekday AI. This role is for one of the Weekday's clients. Min Experience: 7 years. Location: Remote (India). JobType: full-time. As an Applied AI Manager, you will own the end-to-end productionization of AI research into reliable, scalable, and continuously improving production systems. This is a mission-critical, hands-on leadership role that sits at the intersection of AI research and real-world deployment. You will be responsible for ensuring that AI models transition seamlessly from experimentation to stable production and remain performant, cost-efficient, and well-monitored over time. Leading a hybrid team of MLOps engineers and Applied AI scientists, you will act as the primary bridge between research and engineering, owning the full lifecycle of deployed models—from training and inference to monitoring, retraining, and retirement—while driving strong execution standards and cross-functional alignment.. Key Responsibilities. Own and drive the productionization of AI models, managing the transition from research validation to scalable production deployment. Build and operate robust data pipelines, training infrastructure, and evaluation frameworks to support continuous model iteration. Manage the complete lifecycle of production AI models, including training, fine-tuning, deployment, versioning, rollback, A/B testing, monitoring, retraining, and decommissioning. Design and maintain low-latency, cost-efficient inference systems with clearly defined SLAs. Build and scale AI infrastructure capable of supporting both large-scale training workloads and reliable production inference. Partner closely with AI research teams to define model readiness criteria, evaluation standards, and structured handoff processes. Maintain and prioritize a backlog of research models awaiting productionization across stakeholders. Lead, mentor, and grow a high-performing hybrid team of MLOps engineers and Applied AI scientists. Establish execution standards focused on delivery velocity, system reliability, and iterative quality improvement. Build and maintain ML CI/CD pipelines, including experiment tracking, model versioning, observability, and alerting. Contribute to architectural decisions, AI platform roadmaps, and technical documentation. Collaborate with cloud and AI platform providers to integrate tooling, optimize infrastructure costs, and unlock advanced capabilities. Champion best practices in MLOps, data engineering, and applied AI across the organization. What Makes You a Great Fit. 6+ years of experience in MLOps, ML Engineering, Applied AI, or Data Engineering with ownership of production ML systems. Proven experience taking AI models from research or experimentation into large-scale production environments. Strong understanding of machine learning and deep learning fundamentals across the full model lifecycle. Experience leading or mentoring senior engineers and/or applied scientists. Deep expertise in building data pipelines, monitoring systems, and model observability frameworks. Hands-on experience with performance optimization techniques such as quantization, distillation, or accelerated inference frameworks. Strong DevOps mindset applied to ML systems, including automation and reliability engineering. Ability to lead complex technical initiatives end-to-end with minimal oversight. Comfortable balancing exploratory research workflows with production delivery commitments. Strong cross-functional communication skills and the ability to drive clarity in ambiguous problem spaces. Ownership-driven mindset with confidence in decision-making and technical leadership. Company Location: India.