MLOps Engineer / Senior MLOps Engineer – AI, Machine Learning & Geospatial Analytics

Location: Johannesburg, South Africa (Onsite/Hybrid) or PAN India (Remote)
Experience: 5+ Years
Domain: AI/ML, Cloud Platforms, Geospatial & Customer Intelligence
Job Title: MLOps Engineer / Senior MLOps Engineer
Location: Johannesburg, South Africa (Onsite/Hybrid) or PAN India (Remote)
Experience: 5+ Years
Domain: AI/ML, Cloud Platforms, Geospatial & Customer Intelligence
Role Overview
We are seeking a highly skilled MLOps Engineer to design, build, deploy, and support enterprise-scale Machine Learning and AI platforms. The role is responsible for implementing robust MLOps capabilities, automating model lifecycle management, enabling geospatial and customer intelligence solutions, and ensuring secure, scalable, and governed deployment of AI/ML services across cloud platforms.
The successful candidate will work closely with Data Scientists, Data Engineers, Platform Engineers, GIS Specialists, Product Teams, and Business Stakeholders to deliver production-ready analytics and AI solutions.
Key Responsibilities
ML Platform Engineering
Design, build, and maintain enterprise MLOps platforms and operating standards.
Develop model deployment pipelines, model registries, experiment tracking, and version control frameworks.
Build and maintain CI/CD pipelines for ML models, feature pipelines, and inference services.
Containerize workloads using Docker and deploy to Kubernetes and AKS environments.
Implement Infrastructure as Code (IaC) using Terraform or equivalent technologies.
Deploy and operationalize geospatial models, including:
Footfall prediction
Mobility analytics
Origin-Destination modelling
Catchment analysis
Trade area optimization
Site suitability and location scoring
Automate model retraining, validation, release, and monitoring processes.
Customer Intelligence & Advanced Analytics
Deploy customer segmentation, audience clustering, and behavioral profiling models.
Implement churn prediction, propensity scoring, recommendation engines, and customer value models.
Enable automated audience refresh and publishing capabilities.
Package AI/ML models as reusable APIs and microservices.
Data & Feature Engineering
Design reusable feature engineering pipelines and enterprise feature stores.
Implement feature versioning, data lineage, monitoring, and quality controls.
Ensure consistency between model training and production serving environments.
AI Enablement
Enable predictive analytics and AI-driven insights.
Support GenAI, Geospatial AI, and AI-assisted decision-making capabilities.
Implement model evaluation, monitoring, guardrails, and governance controls.
Governance, Compliance & Operations
Implement model governance frameworks, approval processes, and audit controls.
Manage model inventories, documentation, validation evidence, and lifecycle controls.
Monitor model performance, drift, availability, and operational health.
Support production releases, incident management, root cause analysis, and service improvements.
API & Product Integration
Build scalable REST APIs and real-time inference services.
Integrate AI/ML services with portals, dashboards, geospatial platforms, and enterprise applications.
Produce technical documentation and operational runbooks.
Required Qualifications
Bachelor's Degree in Computer Science, Data Science, Engineering, Information Technology, or a related discipline.
5+ years of experience in Software Engineering, Data Engineering, MLOps, or Machine Learning Platform Engineering.
Experience deploying and managing machine learning solutions in production environments.
Strong understanding of software engineering best practices and DevOps principles.
Required Technical Skills
Cloud & Platform Technologies
Microsoft Azure
Azure Machine Learning
Azure Kubernetes Service (AKS)
Azure Databricks
Microsoft Fabric
Azure AI Foundry
Data & Machine Learning
Python
SQL
Apache Spark
MLflow
Machine Learning lifecycle management
Feature Stores and Feature Engineering
Model Monitoring and Drift Detection
DevOps & Automation
Azure DevOps and/or GitHub Enterprise
Git Version Control
CI/CD Pipeline Development
Docker
Kubernetes
Terraform
Infrastructure as Code (IaC)
Monitoring & Operations
Observability and Monitoring Tools
Logging and Alerting Frameworks
Incident Management
Root Cause Analysis
Production Support
Preferred Experience
Telecommunications, Retail Analytics, Financial Services, or Location Intelligence domains.
Geospatial analytics platforms and GIS technologies.
Customer Analytics and Audience Intelligence solutions.
AI Governance, Responsible AI, and Model Risk Management.
Experience with Agentic AI and Generative AI solutions.
Key Competencies
Strong problem-solving and analytical skills.
Excellent stakeholder management and communication abilities.
Ability to work in cross-functional Agile teams.
Strong documentation and knowledge-sharing practices.
Focus on automation, scalability, reliability, and governance.
Key Deliverables
Enterprise MLOps Platform
Automated CI/CD Frameworks
Model Registry and Governance Framework
Geospatial Intelligence Model Deployments
Customer Intelligence & Audience Analytics Solutions
Enterprise Feature Store and Feature Catalog
AI Service Frameworks and APIs
Model Monitoring & Observability Dashboards
Production Support Runbooks and Operational Documentation
Ideal Candidate Profile
A hands-on MLOps professional with strong Azure, Kubernetes, DevOps, and Machine Learning engineering expertise, capable of taking AI and analytics solutions from experimentation through to secure, scalable, enterprise-grade production deployment while ensuring governance, reliability, and business value realization.

Work arrangement
Yes