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