Machine Learning – FEDIP Job Profiles

Fault tolerance (MLEC7.7)(Level Three)

You can design fault-tolerant systems.

Performance monitoring (MLEC7.6)(Level Three)

You can set up monitoring systems to track the performance and health of distributed applications.

Security considerations (MLEC7.5) (Level Three)

You can demonstrate an awareness of security implications in distributed systems, including data privacy and secure communication between distributed components.

Resource management (MLEC7.4)(Level Three)

You know how to monitor resource usage and cost.

Scalability (MLEC7.3)(Level Three)

You have an awareness of scalability issues for large datasets and high compute.

Distributed systems (MLEC7.2)(Level Three)

You can demonstrate knowledge of distributed computing principles, including multi-threading and the use of multiple servers.

Cloud fundamentals (MLEC7.1) (Level Three)

You can demonstrate an understanding of the core concepts of cloud computing, including the use of cloud services like AWS, Azure, and Google Cloud.

Agile methods (MLEC6.11)(Level Three)

You can demonstrate a strong understanding of agile methodologies like Scrum and Kanban.

Development approaches (Machine Learning) (MLEC6.9) (Level Three)

You can use the agreed specifications to design, code, test and document programs or scripts of medium-to-high complexity, using the right standards and tools.

API development (MLEC6.8)(Level Three)

You can demonstrate proficiency in setting up and using APIs.

CI/CD (MLEC6.7) (Level Three)

You can demonstrate knowledge of continuous integration and continuous deployment (CI/CD) pipelines.

Application development (MLEC6.6) (Level Three)

You can develop applications, including front-end interfaces for machine learning models.

Code review and debugging (MLEC6.5)(Level Three)

You can participate in code reviews, and demonstrate proficiency in debugging both code and statistical models.

Non functional requirements (MLEC6.4) (Level Three)

You consider non functional requirements and impact on user experience.

Robust coding practices (MLEC6.3)(Level Three)

You can write robust, well-documented, and maintainable code.

Version control (MLEC6.2) (Level Three)

You can demonstrate a strong understanding and use of source control systems like Git.

Programming skills (MLEC6.1) (Level Three)

You can demonstrate proficiency in programming.

Understanding infrastructure (MLEC5.7)(Level Three)

You can understand the capabilities and limitations of different infrastructures.

Lifetime management (MLEC5.6)(Level Three)

You can consider the entire lifecycle of machine learning products, from deployment to maintenance and updates.

Testing and monitoring (MLEC5.5) (Level Three)

You can implement testing frameworks, and understand issues of monitoring systems and model drift.

Cost-benefit analysis (MLEC5.4) (Level Three)

You can balance cost, benefit, and performance considerations in system integration.

Redundancy and reliability (MLEC5.3) (Level Three)

You consider redundancy and ensure the reliability of components when implementing solutions.

Containerisation and interoperability (MLEC5.2) (Level Three)

You can demonstrate proficiency in containerisation and ensure interoperability between different systems and platforms.

Integration skills (MLEC5.1) (Level Three)

You can create and manage MVP pipelines and interfaces, ensuring seamless integration of machine learning models into existing systems.

Realistic solutions (MLEC4.6) (Level Three)

You can propose and implement innovative solutions that are feasible within the organisation’s resources and capabilities.

Knowledge sharing (Machine Learning) (MLEC4.5)(Level Three)

You proactively share your knowledge and work.

Leadership (MLEC4.4) (Level Three)

You can lead the transformation of innovative ideas into practical applications and projects.

Improvement (Machine Learning) (MLEC4.3)(Level Three)

You can embrace a mindset of continuous development and iterative improvement of models and solutions.

Tools and techniques (Machine Learning) (MLEC4.2) (Level Three)

You can identify and apply innovative tools and techniques within the constraints of the organisation.

Best practice (MLEC4.1) (Level Three)

You can stay current with cutting-edge research and developments in machine learning and related fields.

Interdisciplinary collaboration (MLEC3.5) (Level Three)

You are aware of the need for collaboration with other disciplines such as cybersecurity, data privacy, and legal teams.

Cybersecurity awareness (MLEC3.4)(Level Three)

You can understand the cybersecurity implications of machine learning models and apply that appropriate security measures.

Regulatory compliance (MLEC3.3)(Level Three)

You stay informed about and adhere to relevant AI regulations and legal requirements.

Privacy awareness (MLEC3.2)(Level Three)

You apply the appropriate privacy standards, regulations and secure data handling to models and your data usage.

Ethical considerations (MLEC3.1) (Level Three)

You can understand and apply ethical principles in the development and deployment of machine learning models.

Computer science practices (MLEC2.5)(Level Three)

You can demonstrate proficiency in computer science principles, including algorithms, data structures, and computational complexity.

Research and development (MLEC2.4) (Level Three)

You can keep up with scientific developments, understand research papers, and contribute to research in the field of machine learning.

Application (Machine Learning) (MLEC2.3) (Level Three)

You can apply appropriate statistical techniques to available data to discover new relations and offer insight into research problems, helping to improve organisational processes and support decision making.

Guidance (MLEC2.2) (Level Three)

You can provide guidance on matching data sources with relevant applied mathematics and statistical techniques to meet analysis goals.

Techniques (MLEC2.1)(Level Three)

You are aware and can apply appropriate machine learning models and techniques.

Community links (MLEC1.13) (Level Three)

Develops and maintains links with machine learning colleagues in the wider community of academia and industry.

System context (Machine Learning) (MLEC1.12)(Level Three)

Understands the system context of their product including the identification of stakeholders and appreciation of wider issues.

Benefits and value (Machine Learning) (MLEC1.11) (Level Three)

You are aware of the benefits and value of different solutions.

Standards (Machine Learning) (MLEC1.10) (Level Three)

You have an awareness of the available standards and procedures.

Reporting processes (Machine Learning) (MLEC1.9) (Level Three)

Uses reporting processes to highlight risk and issues and any performance changes.

User needs (Machine Learning) (MLEC1.7) (Level Three)

Evaluates data products to ensure that they meet the needs of a variety of users and utilises feedback for continuous improvement.

Tool selection (Machine Learning) (MLEC1.6) (Level Three)

You have an awareness of the available tools, considering multiple options for tooling and their implications

Improving outputs (Machine Learning) (MLEC1.5) (Level Three)

Leads colleagues to create more advanced outputs.

Tailored presentation (Machine Learning) (MLEC1.4) (Level Three)

Draws out the key messages for the customer and provides insight to inform debate and influence decision making.

Explanation and recommendation (MLEC1.3) (Level Three)

Clearly explains the implications of analytical evidence and makes reasonable recommendations based on the results of analysis.

Key messages (Machine Learning) (MLEC1.2) (Level Three)

Communicates key messages from analytical work in clear and concise terms for a variety of audiences.

Fault tolerance (MLEC7.7) (Level Four)

You can implement fault-tolerant systems to ensure reliability and availability of distributed applications.

Performance monitoring (MLEC7.6) (Level Four)

You can design alerts and incident strategies to deal with changes in performance.

Security considerations (MLEC7.5) (Level Four)

You can design and implement solutions that adhere to security considerations as a first class citizen.

Resource management (MLEC7.4) (Level Four)

You can efficiently manage computational resources to optimise performance and cost.

Scalability (MLEC7.3)(Level Four)

You can design and implement scalable machine learning solutions that can handle large datasets and high computational loads.

Distributed systems (MLEC7.2) (Level Four)

You can demonstrate experience of distributed computing principles, including multi-threading and the use of multiple servers.

Cloud fundamentals (MLEC7.1) (Level Four)

You are certificated or have demonstratable experience of developing solutions in Cloud.

Agile methods (MLEC6.11) (Level Four)

You lead the deployment of agile methodologies like Scrum and Kanban.

Engineering standards (Machine Learning) (MLEC6.10)(Level Four)

You can advise on the right way to apply standards and methods to ensure compliance.

Development approaches (Machine Learning) (MLEC6.9)(Level Four)

You can maintain technical responsibility for all the stages and iterations of a software development project.

API development (MLEC6.8) (Level Four)

You can demonstrate proficiency in setting up and using APIs.

CI/CD (MLEC6.7) (Level Four)

You champion continuous integration and continuous deployment (CI/CD) pipelines.

Application development (MLEC6.6) (Level Four)

You can develop applications, including front-end interfaces for machine learning models.

Code review and debugging (MLEC6.5) (Level Four)

You lead code reviews and debugging, setting the framework for what good looks like.

Non functional requirements (MLEC6.4) (Level Four)

You consider non functional requirements and impact on user experience.

Robust coding practices (MLEC6.3) (Level Four)

You devise and champion robust coding practices.

Version control (MLEC6.2) (Level Four)

You champion version control across the organisation.

Programming skills (MLEC6.1) (Level Four)

You can demonstrate proficiency in programming across multiple languages and platforms, provide technical advice to stakeholders, and set the team-based standards for programming tools and techniques.

Understanding infrastructure (MLEC5.7) (Level Four)

You identify areas of improvement and work to improve infrastructure.

Lifetime management (MLEC5.6) (Level Four)

You design solutions that address lifetime management considerations.

Testing and monitoring (MLEC5.5) (Level Four)

You devise testing frameworks and lead on monitoring systems and model drift.

Cost-benefit analysis (MLEC5.4) (Level Four)

You lead on balancing cost, benefit, and performance considerations in system integration.

Redundancy and reliability (MLEC5.3) (Level Four)

You ensure redundancy and reliability in solutions.

Containerisation and interoperability (MLEC5.2) (Level Four)

You lead on containerisation and ensure continuous improvement between different systems and platforms.

Integration skills (MLEC5.1) (Level Four)

You lead on continuous improvement and managing the migration of tools from one system to another.

Realistic solutions (MLEC4.6) (Level Four)

You focus on exploring and promoting possible alternative solutions that would work within the tech stack, emphasising forward-looking but practical approaches.

Knowledge sharing (Machine Learning) (MLEC4.5) (Level Four)

You can act as a coach, inspiring curiosity and creativity in others.

Leadership (MLEC4.4) (Level Four)

You can be a leader in the Machine Learning community.

Improvement (Machine Learning) (MLEC4.3)(Level Four)

You create an environment for and model a mindset of continuous development and iterative improvement of models and solutions.

Tools and techniques (Machine Learning) (MLEC4.2) (Level Four)

You can demonstrate in-depth knowledge of machine learning tools and techniques, which you can use to solve problems creatively and to create opportunities for your team.

Best practice (MLEC4.1) (Level Four)

You can demonstrate in-depth knowledge of new developments and best practice and ensure you that your team members are kept up to date with changes in the industry.

Interdisciplinary collaboration (MLEC3.5) (Level Four)

You ensure comprehensive compliance through active collaboration with other disciplines like cybersecurity, data privacy, and legal teams to ensure comprehensive compliance and security.

Cybersecurity awareness (MLEC3.4) (Level Four)

You actively engage with cybersecurity colleagues and ensure that appropriate security measures are in place.

Regulatory compliance (MLEC3.3) (Level Four)

You ensure adherence to relevant AI regulations and legal requirements.

Privacy awareness (MLEC3.2) (Level Four)

You can ensure that models and data usage comply with privacy standards and regulations, and that data is handled securely.

Ethical considerations (MLEC3.1) (Level Four)

You lead on and champion the application of ethical considerations in the development and deployment of machine learning models, within the team and broader organisational context.

Computer science practices (MLEC2.5) (Level Four)

You understand the application of computer science practices in the healthcare specific context and lead on improving the technology stack.

Research and development (MLEC2.4) (Level Four)

You can keep up with scientific developments, understand research papers, and contribute to research in the field of machine learning.

Application (Machine Learning) (MLEC2.3) (Level Four)

Guidance (MLEC2.2)(Level Four)

You can provide guidance on matching data sources with relevant applied mathematics and statistical techniques to meet analysis goals.

Techniques (MLEC2.1)(Level Four)

You have a deeper understanding and are able to discuss the robustness and context of the machine learning models and techniques.

Community links (MLEC1.13) (Level Four)

Develops and maintains links with machine learning colleagues in the wider community of academia and industry.

System context (Machine Learning) (MLEC1.12) (Level Four)

Understands the system context of their product including the identification of stakeholders and appreciation of wider issues.

Benefits and value (Machine Learning) (MLEC1.11) (Level Four)

You can communicate the benefits and value of different solutions.

Standards (Machine Learning) (MLEC1.10) (Level Four)

You lead and advise on the adoption of available standards, procedures, methods, tools and techniques.

Reporting processes (Machine Learning) (MLEC1.9) (Level Four)

Ensures that reporting processes are robust, efficient and fit for purpose.

User research (MLEC1.8) (Level Four)

Understands how to integrate findings from user research and collaborates effectively with colleagues from these professions to deliver enhanced products.

User needs (Machine Learning) (MLEC1.7)(Level Four)

Develops plans demonstrating how user needs will be met.

Tool selection (Machine Learning) (MLEC1.6) (Level Four)

You lead on and support the team in determining the available tooling and can make balanced and pragmatic recommendations, based on the known associated positives and negatives.