Job Purpose

We are looking for a motivated ML Engineer to the implementation of enterprise-level AI solutions.

design, develop, and deploy advanced ML/AI Solutions that solve business problems and drive growth across the organization.

Working closely with our Data Scientists, Platform, and Cloud teams, you will turn ML/AI prototypes into production-ready ML/AI solutions, build automated model pipelines, and help engineer the MLOps infrastructure that supports the implementation of enterprise level ML/AI systems at scale across our AWS, GCP, and Azure environments.

The Job

• Analyse business requirements and work with Data Scientists to define model structure, decision boundaries, and the right tools to solve the problem.
• Convert data science prototypes into deployable, production-ready machine learning models.
• Define data pre-processing and feature engineering, train models, tune hyperparameters, and design validation strategies.
• Develop the stack for deploying models into production, defining inference methods and maintaining connectivity with serving infrastructure.
• Design and orchestrate automated CI/CD/CT ML pipelines using cloud tool stacks such as Amazon SageMaker Pipelines and AWS CodePipeline.
• Build and maintain production-grade Python libraries, following best practices in version control and continuous integration/delivery (e.g., GitLab, CodeCommit).
• Design and develop containerized model artifacts using Docker and Kubernetes.
• Monitor and troubleshoot in-production models by analysing model errors, designing corrective strategies, and maintaining monitoring and alerting systems.
• Apply model governance practices, including model versioning, registration, lineage, performance tracking, and explainability.
• Manage the lifecycle of AI solutions, ensuring efficient processes, security, and smooth day-to-day operation.
• Research, evaluate, and implement new MLOps tools, frameworks, and optimization methods, and help develop MLOps best practices for the team.
• Collaborate with Data Scientists, Engineers, Business Units, and other stakeholders in a fast-paced, Agile, cross-functional environment.

The Person

• Bachelor's Degree in IT/ICT, Computer Science, Artificial Intelligence, Data Science, Statistics, Software Engineering, or a related discipline.
• 0 to 2 years of experience in Machine Learning Engineering, MLOps, Data Science, or related roles.
• Good understanding of ML and AI concepts, with hands-on experience in ML model development, including models for tabular data and an understanding of Computer Vision and NLP.
• Proficiency in Python (or at least one programming language) for machine learning and automation tasks.
• Working knowledge of ML frameworks and libraries such as PyTorch, Keras, and scikit-learn.
• Experience operationalizing data science projects (MLOps) using at least one platform such as Amazon SageMaker, Google Cloud Vertex AI, or Azure Machine Learning.
• Experience implementing CI/CD/CT pipelines and working with containerization (Docker, Kubernetes) and version control.
• Experience with cloud platforms (AWS, GCP, or Azure) and exposure to Generative AI or Agentic AI solutions will be an added advantage.
• Familiarity with Agile/Scrum practices, with strong problem-solving, teamwork, and communication skills.