Overview

This occupation is found in a wide range of public and private sector organisations who increasingly work with machine learning (ML) systems and AI automation that can serve all industries and sectors such as agriculture, environmental and animal care, business and administration, care services, catering and hospitality, construction and the built environment, creative & design, digital, education, engineering & manufacturing, health and science, legal, finance and accounting, protective services, sales, marketing and procurement, transport and logistics.

ML Engineers gather data from different sources to design, build, deploy and validate machine learning and or artificial intelligence solutions. They ensure that data is sourced responsibly and analysed to a high standard, aligning the use of ML solutions with the organisations business goals. They build ML models in an innovative, safe and sustainable way, selecting features that will help the model learn effectively by using the right algorithm for the task. Once the ML model is trained, they evaluate its performance and deploy it into the live environment. They streamline the process of taking ML models into production, and then maintain and monitor them. Continuous monitoring is essential to maintain the ML models accuracy. They manage the lifecycle of ML systems & models from initial deployment, to testing and updating of the next iteration, using industry best practice and frameworks to ensure fast, simple and reliable ML pipelines. They would identify as AI professionals, conversant in operating in settings of technical complexity and uncertainty. They can interface effectively across the organisation to communicate the correctness of their engineered technical solutions.

A ML engineer will work with a variety of professionals who work together to facilitate the successful development, deployment and adoption of ML systems and models, working with minimal supervision, ensuring they are meeting deadlines and interacting with Data Scientists for analytical guidance, Data Engineers for data preparation, Software Engineers for integration, Product Managers for product strategy, QA Engineers for testing, DevOps Engineers for deployment, UI/UX Designers for user interface design, Business Analysts for requirement analysis and stakeholders or clients for feedback and updates. They typically report to either the Senior ML Operations Engineer, Product Manager ML, AI Specialist, AI Engineering Manager or Client.

A ML engineer will provide clear technical support communicating complex information to stakeholders and across the organisation inputting into systems documentation, with details around risks and potential mitigation actions in line with the correct organisational standards. They are responsible for meeting quality requirements and working in accordance with health and safety and environmental considerations. They will work according to organisational procedures and policies, to maintain security and compliance and be responsible for ensuring compliance with data governance, ethics, environmental, sustainability and security policies.

Apprenticeship Assessment

Each assessment organisation is responsible for designing its own assessments in line with the apprenticeship standard. The Academy for Project Management has developed an assessment model that allows apprentices to demonstrate competence against the required assessment outcomes. Comprehensive guidance and resources are provided to support both apprentices and training providers.

The assessment for the Machine Learning Engineer includes:

• At least one project

And the choice of a:

• professional discussion
• additional project
• presentation
• portfolio
• question and answer
• written assessment

The standard also includes Employability Skills and Behaviours (EBs), which must be demonstrated consistently by the apprentice throughout the programme. Employers are required to formally confirm this before completion.

Assessment Timing

Components of the assessment may be scheduled at appropriate points during the apprenticeship. The Academy for Project Management works closely with training providers to ensure assessments are timed effectively, allowing apprentices to demonstrate their skills and knowledge at key stages in their development.

Additional information on the apprenticeship standard and the assessment process can be viewed here.

Becoming a Centre

Training providers interested in delivering elements of the assessment internally can explore the Centre model with us. Guidance is available on how the model operates and the steps required to become an approved Centre. For more information, please contact administration@academy4pm.com