AI Machine Learning Engineer

Job Description - AI Machine Learning Engineer

SECTION A: POSITION SUMMARY

State the objective and purpose of the role.

Strong MLOps and production operations focus

Responsible for managing MLOps workflows, tools, and production support processes for ML solutions.

Ensure day-to-day stability, reliability, and performance of ML models and pipelines.

Manage model lifecycle controls, including versioning, lineage, reproducibility, monitoring, and goverce.

Develop incident handling, recovery, and escalation procedures for ML-related issues.

Support data quality, lineage tracking, and goverce practices across the ML lifecycle.

SECTION B: KEY RESPONSIBILITIES AND RESULTS

Indicate key responsibilities and performance indicators of this role.

For existing role, please indicate additional responsibilities in bold.

1. Responsible for designing, implementing, and managing MLOps workflows, tools, and operational processes for AIML solutions. 2. Oversee the day-to-day stability, reliability, and operational health of ML models and ML pipelines. 3. Manage model lifecycle operations, including model registration, versioning, deployment tracking, lineage, reproducibility, and goverce. 4. Implement monitoring for data drift, concept drift, model performance degradation, inference quality, and service-level issues. 5. Develop and execute incident handling, recovery, rollback, and escalation plans for ML-related issues.

SECTION C: QUALIFICATIONS / EXPERIENCE / KNOWLEDGE REQUIRED

Indicate key knowledge and skills required for this role to perform the tasks to a satisfactory level. To also specify a suitable level of qualification required (i.e. basic, advanced, or professional), where applicable.

Category Essential for this role Good to have Education and Qualifications Bachelor s or Master s degree in Computer Science or a related field Work Experience

1. Experience with MLOps processes and tools

2. Experience with SQL, Databricks, MLFlow and PowerBI/Tableau.

Technical / Professional Skills

Please provide at least 3

1. Hands-on experience with MLOps tools and cloud ML platforms such as MLflow, Databricks, Azure ML, or equivalent.

2. Strong SQL and data analysis skills for validation, troubleshooting, monitoring, and reporting.

3. Experience with model lifecycle management, including model registry, versioning, lineage, reproducibility, deployment tracking, and monitoring.

4. Familiarity with dashboards and alerting tools such as Power BI, Tableau, Databricks SQL dashboards, or equivalent.

5. Working knowledge of Git, CI/CD, scripting, and production support practices would be advantageous.

Non-Technical / Soft Skills

1. Analytical and pragmatic, with the ability to interpret goverce principles into implementation plans

2. Clear communicator who can explain complex technical risks and solutions to non-technical stakeholders

3. Self-driven and proactive, comfortable working in a fast-paced environment

Other Task-Specific Knowledge 1. Familiarity with ML and data development process in telco environment

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