Manager – ML Engineer.DataCo needed at MTN

Responsibilities

ML Engineering & Pipeline Development

• Build and optimise end-to-end machine learning pipelines (feature extraction, training, scoring, monitoring).
• Convert Data Scientist prototypes into production-grade model code and scalable pipelines.
• Implement CI/CD for ML using tools such as MLflow, Kubeflow, Airflow, Azure ML, or GCP Vertex AI.

MLOps, Deployment & Automation

• Deploy ML models across cloud, hybrid, or on-prem environments based on DataCo standards.
• Automate model retraining, versioning, rollout, rollback, and dependency management.
• Ensure reproducibility, auditability, and governance of AI pipeline

Model Monitoring, Observability & Governance

• Implement model monitoring dashboards tracking drift, latency, accuracy, and fairness.
• Build alerting mechanisms for model degradation or operational failure
• Maintain compliance with MTN Responsible AI, privacy and security standards.

Data Engineering & Integration Support

• Collaborate closely with Data Engineering teams to ensure model-ready data pipelines.
• Build feature stores, data transformation pipelines, or reusable data components supporting ML workloads.
• Optimise data ingestion workflows for high-volume telco, geospatial and behavioural datasets.

Cross-functional Collaboration & Delivery

• Work within agile squads alongside Data Scientists, Product Owners, Engineers, and Delivery teams.
• Participate in sprint ceremonies and contribute to backlog refinement.
• Translate technical designs into scalable solutions for internal users and external clients.

Documentation & Enablement

• Produce technical documentation covering ML architectures, APIs, pipelines, monitoring, and deployment logic.
• Provide technical onboarding and support to OpCo teams adopting DataCo ML solutions.
• Contribute to AI/ML best-practice frameworks, toolkits, and internal knowledge bases.

Qualifications

Education:

• 3 year Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, AI/ML, or related field
• Postgraduate qualification preferred
• Certifications in cloud platforms (Azure, GCP, AWS), MLOps, or ML engineering advantageous

Experience:

• 4–6 years’ experience in ML engineering, MLOps, or machine learning deployment
• Experience building ML pipelines using cloud-native tools (Azure ML, GCP Vertex AI, Databricks, MLflow, Kubeflow)
• Demonstrated experience deploying ML models to production in enterprise environments
• Strong experience in Python, Spark, CI/CD (GitHub Actions, Jenkins), containerisation (Docker, Kubernetes)
• Exposure to telco, geospatial, behavioural or large-scale datasets advantageous
• Prior experience in cross-functional, agile delivery squads

Competencies:

• Strong ML engineering capability (scalable pipelines, APIs, cloud ML)
• Proficiency in Python, PySpark, SQL, Big Data frameworks
• Deep knowledge of CI/CD, DevOps and MLOps tooling
• Experience with monitoring, drift detection, logging, and observability
• Understanding of Responsible AI practices and compliance frameworks
• Problem Solver & Innovative Thinker
• Results-Driven & Operationally Astute
• Cross-Functional Collaborator
• Strong Communicator (technical & non-technical)
• Agile mindset, adaptable to evolving environments

Key Deliverables

Internal Deliverables

• Production-ready ML pipelines with CI/CD integration
• Automated monitoring dashboards and model performance logs
• Feature stores and reusable pipeline modules
• Deployment playbooks, runbooks, and engineering documentation
• Scalable inference environments powering Group and OpCo use cases

External Deliverables

• ML deployment assets used in DataCo consulting engagements
• API-based ML services for data monetisation products
• Technical documentation and integration guides for clients
• Model hosting & monitoring frameworks enabling client applications

Skills

• Strong proficiency in BI tooling (Power BI/Tableau), SQL, data modelling.
• Understanding of data engineering pipelines and cloud data environments.
• Familiarity with AI/ML concepts and analytics productization beneficial.
• Data governance, metadata, and KPI standardisation experience.