MLOps Support Team Lead Job CloudFactory
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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
How to Apply
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