Job Title / Designation: AI / Machine Learning Engineer – Data Lake Analytics
Number of Vacancies: 2
Functional Area: Software Development / Data Engineering & AI
Industry: IT
Location of Job: Kampala, Uganda
Salary Offered: Negotiable
Industry: IT Software & Services
Qualification: Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, or a related field.
Contact Person: HR Manager: careeruganda@sybyl.com
Role Overview.
We are looking for an AI / Machine Learning Engineer – Data Lake Analytics to join our Software team in Kampala. Reporting to the General Manager – Software Solutions, you will design, develop, and deploy machine learning solutions that leverage our enterprise data lake. You will work closely with data engineers, business analysts, and domain experts to turn large volumes of raw data into intelligent, production-ready models that drive decision-making, automation, and business value across the organization.
Responsibilities
Data Lake & Data Engineering Collaboration
Work closely with data engineers to understand data lake structures, schemas, and ingestion processes.
Help define data requirements for AI/ML use cases and ensure the necessary data is available, clean, and well-documented.
Optimize data access patterns (e.g., using Spark, SQL, and data lake table formats) for scalable training and inference.
AI/ML Solution Development
Design, build, and deploy machine learning models using data from the enterprise data lake.
Perform data exploration, feature engineering, and model selection to solve specific business problems.
Develop reusable components, feature pipelines, and model templates for repeatable AI/ML use cases.
MLOps & Productionization
Package, deploy, and monitor machine learning models in production (batch and/or real-time).
Implement model versioning, experiment tracking, and performance monitoring using MLOps tools (e.g., MLflow, Kubeflow, Airflow, CI/CD).
Work with DevOps / platform teams to ensure high availability, scalability, and reliability of AI solutions.
Governance, Quality & Compliance
Ensure models adhere to data governance, privacy, and security policies.
Document model assumptions, limitations, and performance metrics clearly for business and audit purposes.
Contribute to responsible AI practices, including bias checks, explainability, and fairness.
Stakeholder Engagement
Collaborate with business teams to understand use cases and translate them into technical requirements.
Communicate model results, insights, and recommendations in a clear, non-technical way.
Present outcomes and impact to stakeholders and senior management.
Required Skills & Experience
Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, or a related field.
2+ years of hands-on experience in building and deploying machine learning models in a production environment.
Strong programming skills in Python (preferred) and solid knowledge of SQL.
Practical experience with ML frameworks such as scikit-learn, TensorFlow, PyTorch, XGBoost, or similar.
Experience working with big data / data lake environments, such as:
Apache Spark / PySpark
Cloud object storage or data lake platforms (e.g., AWS S3, Azure Data Lake, GCP Cloud Storage, Delta Lake, Iceberg, Hudi)
Query engines (e.g., Hive, Presto, Trino, Athena, BigQuery, Synapse, etc.)
Experience building end-to-end ML pipelines: data preparation, training, validation, deployment, and monitoring.
Good understanding of statistics, probability, and core machine learning concepts.
Preferred / Nice-to-Have Skills
Experience with MLOps tools and practices (e.g., MLflow, Kubeflow, Airflow, Docker, Kubernetes, CI/CD tools).
Experience with real-time / streaming data (e.g., Kafka, Kinesis).
Domain experience in Banking, Financial Services, or related sectors.
Knowledge of NLP, time series forecasting, or graph analytics.
Familiarity with data governance, data quality, and regulatory requirements.
