Senior AI (Artificial Intelligence)/ML (Machine Learning) Engineer

Job description

Job responsibilities

In this role, you are accountable for: AI Engineering and development 1. Designing, developing, and deploying AI solutions from prototype to production, ensuring scalability, security, and maintainability.2. Collaborate on feature engineering, data preprocessing and documentation using industry standards (e.g., model cards, data sheets) to improve model accuracy and transparency.3. Contributing to shared libraries and frameworks that support model training, evaluation, and deployment.4. Applying SOLID principles, design patterns, and dependency injection to AI systems, ensuring maintainability and robustness.5. Owning and enhancing the AI/ML tooling ecosystem including experiment tracking, data pipelines, data access, model deployment pipelines, CI/CD for ML, and ensure deployed models are continuously monitored using observability tools to detect performance degradation, data drift, and operational issues.6. Conducting technical design reviews and implement robust testing and validation of both code and AI/ML models, ensuring quality, reproducibility, performance and adherence to groups and industry best practices.7. Applying secure coding practices and contributes to risk assessments for AI systems, including data privacy, model robustness, and adversarial threats.8. Rapidly experimenting with new AI tools and models (for example, building generative AI agents or other ML-driven automation) to solve business problems. Youll evaluate emerging technologies and integrate the best solutions quickly to continuously improve our services.AI governance, ethics and security9. Ensuring responsible and complaint AI development with ethical, privacy and regulatory frameworks and standards10. Applying bias detection and mitigation techniques to promote fairness and inclusivity in AI models.11. Ensuring model decisions are explainable and auditable using industry-standard tools and techniques (eg SHAP, LIME).12. Supporting evaluation of third-party AI tools and vendors, ensuring alignment with ethical, technical, and procurement standards.

Collaboration and communication 13. Collaborating across cross-functional teams (including data scientists, data engineers, analysts, service designers and other engineers) using agile practices and feedback loops to deliver user centres AI solutions that are, scalable, inclusive, accessible and aligned with public sector outcomes.14. Listening to and interpreting the needs of technical and non-technical stakeholders, and managing their expectations15. Producing well-written and technically sound reports and providing constructive review and assurance of reports authored by others.16. Educate stakeholders on AI system capabilities, limitations, and ethical considerations.

Continuous learning and innovation 17. Evaluate models and systems for cost efficiency, performance and scalability against standard benchmarks.18. Stay ahead of developments in GenAI (e.g. LLMs, multimodal models), ML frameworks (e.g. PyTorch, TensorFlow, Scikit-learn), orchestration tools (e.g. LangChain, LangFuse), model hubs. Taking responsibility for your own continuous professional development.19. Fostering a culture of responsible innovation and continuous learning.20. Supporting the development of talent within the team by delivering specialist training and providing ongoing coaching and mentoring, including promoting best practices in coding, model development, and MLOps (e.g. code review standards, model evaluation and monitoring, data ethics).

Finance and Change 16. Contribute to continuous business plans including budget monitoring17. Contribute to and prepare proposals for change including producing necessary estimates, mandates, and business cases within the relevant departments In addition to the above accountabilities, as post holder you are expected to: 1. Undertake additional duties and responsibilities in line with the purpose of your role and as agreed by your line manager.2. Demonstrate NHSBSA values and core capabilities in all aspects of your work.3. Encourage an environment where your own and colleagues safety and well-being is promoted.4. Contribute to a culture which values diversity and inclusion.5. Follow NHSBSA policies, procedures, and protocols as they apply to your role.Working relationships

Responsible to: DDaT People Manager and AI Lead Key relationships and connections: 1. AI Lead2. Data Colleagues3. AI Team

Person Specification

Personal Qualities, Knowledge and Skills

• A strategic thinker with a delivery mindset--able to scope, prioritise and drive AI initiatives effectively
• Strong intellectual curiosity and an inquisitive approach to emerging technologies and strategic challenges.
• Positive, proactive approach to teamwork and problem-solving.
• Personally motivated, proactive and resilient in navigating ambiguity and complexity.
• Committed to continuous professional development in AI and emerging technologies.
• Promotes best practices in coding, model development, and MLOps.
• Shares expertise and mentors colleagues and engineers.
• Excellent understanding of model lifecycle, from experimentation to production.
• Strong grasp of software engineering principles (SOLID, design patterns), dependency injection, and scalable system design.
• Strong understanding of NLP, embeddings, and semantic search.
• Awareness of ethical and privacy implications of AI including bias, fairness, and transparency.
• Awareness of ISO/IEC 42001:2023 - Artificial Intelligence Management System (AIMS).
• Strong understanding of statutory legislation relevant to AI, including freedom of information, data protection and equalities.
• Knowledge of cloud platforms used for AI (eg AWS, Azure) and MLOps tools (e.g., MLflow, Docker).
• Understanding of data pipelines, feature engineering, and data quality considerations in ML workflows.
• Familiarity with LLM orchestration frameworks (e.g., LangChain, LlamaIndex).
• Understanding of system design and secure deployment practices.
• Strong software engineering and machine learning skills. This includes proficiency in Python and other languages (eg Typescript etc Go, C#, Rust etc)
• Proficiency in at least one programming language (eg Python, Scala etc) plus SQL . Proficiency in ML libraries (e.g., TensorFlow, PyTorch, Hugging Face).
• Ability to set up and use APIs and integrate AI components into existing systems and workflows.
• Ability to collaborate across disciplines such as with data scientists, data engineers, analysts, and other developers.
• Strong ability to lead technical work and communicate complex AI concepts to non-experts.
• Ability to translate functional requirements into technical solutions.
• Ability to critically appraise AI solutions and advise on strategic options and risks.
• Ability to quickly learn or adopt new tools or techniques, including participation in hackathons, experimentation with open-source projects, and rapid prototyping using emerging AI APIs.
• Ability to foster a culture of continuous learning and responsible AI use.

• Active interest in the AI/tech community.
• Understanding of agile methodologies like Scrum and Kanban

Experience

• Significant hands-on experience with state-of-the-art AI technologies and frameworks such as Scikit, prompt engineering, LangChain, vector databases, graph databases, Lakehouse and retrieval-augmented generation (RAG).
• Hands-on experience implementing GenAI solutions in production, including LLMs, prompt engineering, RAG architectures, LangChain, and vector databases (e.g., Pinecone, FAISS, Milvus).
• Proven hands on experience delivering and monitoring production grade AI/ML systems on cloud platforms such as AWS, GCP or Azure, ensuring ethical, legal and regulatory compliance.
• Hands on experience with ML tooling and infrastructure such as MLflow, Snowflake, Databricks, SageMaker or Azure Machine Learning and automation tools (e.g., Terraform, Python).
• Experience of CI/CD pipelines and source control systems (eg Git).
• Strong hands-on experience with supervised, unsupervised, and generative models.
• Experience working with large, complex datasets and AI models in real-world environments.
• Evidence of initiating improvements (automating a manual process, testing a new library, etc.).
• Experience mentoring or developing AI engineering capability within the team.

• Experience of organisations which prioritise strategic, high impact applications of data and AI.
• Exposure to adjacent domains like web application development, data engineering, or DevOps which helps in integrating AI solutions smoothly into broader systems.

Qualifications

• Degree in a relevant discipline (e.g. Computer Science, Machine Learning, Artificial Intelligence, Data Science).
• Evidence of continuous professional development in AI, machine learning, or related fields.

• Master's degree or higher in Computer Science, Machine Learning, Data Science, Artificial Intelligence or a related discipline.
• Certification in cloud AI services, AI ethics, governance, or strategy (e.g. ISO/IEC, AI4People, Responsible AI frameworks).

Employer details

Employer name

NHS Business Services Authority

Address

Stella House

Goldcrest way, Newburn Riverside

Newcastle Upon Tyne

NE15 8NY

United Kingdom

Employer's website

https://careers.nhsbsa.nhs.uk/