Senior Software Developer (Python AI ML Specialist) - Registers of Scotland - SEO
Government Digital & Data -
Location
Hybrid working model. Contractual base either at Meadowbank House, Edinburgh (EH8 7AU), or St Vincent Plaza, Glasgow (G2 5LD). You will be expected to attend one of these locations as required by the role.
About the job
Job summary
Total remuneration: £58,252-£68,586
Pay Supplement: The base salary for this role is £48,544-£57,155. This job qualifies for Digital, Data and Technology Annual Pay supplement 20% is included in the total remuneration above.
Pension: 28.97% of base salary (RoS contribution)
Annual leave: 38 days annual holiday, increasing to 42 days with length of service
Duration: Permanent
Working Pattern: 35 hours per week. We are a flexible employer and will consider a variety of working patterns on a case-by-case basis. For example, compressed hours, term-time working or part-time working.
Location: Hybrid working model. Contractual base either at Meadowbank House, Edinburgh (EH8 7AU), or St Vincent Plaza, Glasgow (G2 5LD). You will be expected to attend one of these locations as required by the role.
Grade: Senior Executive Officer (SEO)
Closing date: 8 March at 11.59pm
Number of vacancies: 2
Registers of Scotland (RoS)
Join an award-winning organisation recognised for its technology and innovation. Registers of Scotland is a world-leading pioneer in land and property registration. Our full-stack teams design, architect, and build all our registration products in-house. We work to create digital solutions for the people of Scotland. You will get an opportunity to nurture your creativity and develop with us through access to the latest data, software engineering and product delivery techniques.
Job description
The Role
This post sits within the Senior Software Engineer job family and provides specialist expertise in Python and AI/ML engineering day-today. In this role, you will design, build, maintain and support robust software solutions that underpin our digital products and internal services. You will be responsible for developing and operating scalable data pipelines, APIs and cloud-native infrastructure, and for applying AI/ML techniques, including OCR, large language models and computer vision, to automate processes and improve efficiency.
Working across the full delivery lifecycle, you will contribute to discovery, design, implementation, testing, deployment and ongoing support. You will collaborate closely with multidisciplinary teams, ensuring solutions are secure, reliable, maintainable and aligned to architectural and engineering standards, while continuously improving performance and user outcomes.
On a typical day you will…
- Design, build, and operate scalable ETL and data pipelines handling structured and unstructured data for AI/ML workloads.
- Develop and maintain robust API services, including FastAPI, RESTful APIs, WebSockets, model-serving endpoints, integrating AI/ML capabilities with existing digital platforms.
- Implement authentication/authorisation using JWT, OAuth 2.0, API keys, and maintain API versioning and documentation.
- Deploy and operate cloud-native infrastructure using AWS Lambda, S3, RDS/Aurora, SQS, IAM, CloudWatch, with infrastructure-as-code tools: CDK, Terraform, CloudFormation.
- Containerize applications using Docker, orchestrate with Kubernetes (EKS/ECS), and maintain automated CI/CD pipelines.
- Implement monitoring and observability using CloudWatch, Grafana, telemetry frameworks, including experiment tracking tools like MLflow and Weights & Biases.
- Research, prototype, and implement AI/ML solutions using Transformers/Hugging Face, PyTorch, OpenCV, PIL/Pillow, YOLO, including LoRA/QLoRA fine-tuning, RLHF, and -multi-modal AI/ML systems.
- Collaborate with team members to optimize platform and AI/ML workflow performance, reliability, and scalability.
- Ensure compliance with security, accessibility, performance, and operational standards.
- Participate in agile ceremonies, contribute to team knowledge-sharing, and support process improvements.
- Support disaster recovery procedures and maintain high-availability, resilient system standards.
Person specification
Key Responsibilities
Essential Criteria – Skills and Attributes for Success
Technical:
We will assess you against the following Technical and Experience criteria during the application and assessment process:
- Python 3.9+, object-oriented programming, async/await, decorators, context managers, structured logging, pytest, performance optimization.
- Data processing: Pandas, NumPy, SQL, SQLAlchemy/psycopg2, ETL orchestration (Apache Airflow, Dagster, Temporal.io).
- AI/ML frameworks: Transformers/Hugging Face, PyTorch, OpenCV, PIL/Pillow, YOLO; model fine-tuning (LoRA/QLoRA), RLHF, experiment tracking (MLflow, Weights & Biases).
- Web/API development: FastAPI, RESTful APIs, WebSockets, authentication/authorisation (JWT, OAuth 2.0, API keys), API versioning, documentation, model-serving endpoints.
- Cloud & DevOps: AWS Lambda, S3, RDS/Aurora, SQS, IAM, CloudWatch; infrastructure as code with CDK, Terraform, CloudFormation; Docker, Kubernetes (EKS/ECS); CI/CD pipelines.
- Monitoring & Observability: CloudWatch, Grafana, telemetry frameworks for production systems.
- System Design: Event-driven and microservices architectures, high availability, resilient systems, multi-modal AI/ML systems.
- Professional software engineering practices: Git workflows, unit/integration testing, code review, agile delivery (Scrum/Kanban).
Experience
- Developing production-grade AI/ML and data platforms, ensuring reliability, maintainability, and performance for public sector services.
- Designing, building, and operating scalable ETL/data pipelines handling structured and unstructured data.
- Delivering secure, cloud-native AI solutions, integrating with existing infrastructure, managing lifecycle via IaC.
- Developing, supporting, and integrating APIs and microservices, including AI/ML model-serving endpoints.
- Deploying and operating containerized applications in production, with automated CI/CD and environment management.
- Implementing monitoring, alerting, and incident response processes for production systems, including AI/ML services.
- Applying professional software engineering practices collaboratively in multidisciplinary teams to deliver services iteratively.
