CloudFactory is changing the way the world works by providing an on-demand, digital workforce for scaling critical business processes in the cloud. We’re also on a mission to create meaningful work for as many people as possible.Role Summary As the MLOps Operations Lead, you will own the day-to-day reliability, supportability, and operational maturity of CloudFactory's MLOps service. You will lead a global support team responsible for monitoring, triaging, and resolving issues across production ML systems, while driving improvements in observability, incident management, and service delivery. You will work closely with Engineering, Platform Ops, and external partners to ensure AI/ML solutions are not only functional, but stable, measurable, and trusted in production. This role is critical in transitioning MLOps from reactive support to a proactive, scalable service capability. Responsibilities: Service Ownership & Reliability Own the operational performance of all production ML systems and pipelines Ensure reliability, availability, and supportability across client and internal MLOps workloads Establish and enforce SLAs, SLOs, and operational standards Act as the escalation point for major incidents and service degradation Team Leadership & Delivery Lead a global MLOps Support team (L1/L2) across regions (Colombia, Kenya, Nepal) Define shift patterns, on-call rotations, and coverage models Set clear expectations, performance metrics, and development plans Foster a strong operational culture focused on accountability and continuous improvement Incident Management & RCA Own incident response processes, including triage, communication, and resolution Ensure high-quality Root Cause Analysis (RCA) and follow-through on corrective actions Drive reduction in repeat incidents through structured problem management Improve time to detect (TTD) and time to resolve (TTR) metrics Monitoring, Observability & MLOps Maturity Drive implementation and evolution of monitoring across: pipelines and data flows infrastructure and compute model performance and drift Ensure visibility extends beyond system health to model accuracy, bias, and data integrity Partner with Engineering to improve instrumentation, logging, and alerting Support Model & Process Design Define and evolve the MLOps support operating model Clearly establish boundaries between Support, Engineering, and external partners Build and maintain runbooks, playbooks, and escalation paths Standardize intake, triage, and resolution workflows (e.g. Slack, ticketing systems) Stakeholder & Partner Management Act as the primary operational interface for: Engineering teams Platform Operations External partners Reduce reliance on individuals by formalizing ownership and knowledge sharing Provide clear communication during incidents and service updates Continuous Improvement & Scaling Identify trends in incidents and operational inefficiencies Drive improvements in: automation alert quality self-healing capabilities Support onboarding of new MLOps projects into a standardized support model Contribute to building MLOps as a scalable, repeatable service offering Reporting & Service Health Define and track key operational metrics: incident volume and severity SLA adherence system uptime and reliability Support regular service reviews and model health reporting Provide leadership visibility into risks, trends, and improvement areas Requirements Must Have skills (required) Proven experience in operations leadership, SRE, DevOps, or platform support environments Strong understanding of production support models, incident management, and escalation frameworks Experience leading or mentoring technical support or operations teams Working knowledge of ML systems in production, including: pipelines and batch processing model lifecycle and deployment common failure modes Strong analytical and troubleshooting skills in complex environments Experience with monitoring and observability tools Proficiency in: SQL Python or scripting (Bash) Ability to operate in a high-pressure, incident-driven environment while maintaining structure and clarity Strong stakeholder management and communication skills Nice To Have Skills (Preferred) Experience supporting AI/ML platforms at scale Familiarity with tools such as: Databricks MLflow Grafana Power BI New Relic Exposure to model monitoring (drift, bias, performance validation) Experience working with external partners or vendors in delivery models Understanding of cloud platforms (AWS, GCP, Azure) Experience with containerized environments (Docker / Kubernetes) Background in building or scaling support functions from early-stage to maturity General Requirements Strong service ownership mindset — takes accountability for outcomes, not just activity Calm, structured, and decisive during incidents Ability to balance operational delivery with strategic improvement Passion for building reliable, trustworthy AI/ML systems Highly collaborative across Engineering, Platform, and Delivery teams Focus on reducing risk related to: modeil performance bias data integrity Commitment to documentation, knowledge sharing, and eliminating single points of failure