Job Summary

We seek an experienced and forward-thinking Software & AI Manager to lead the design, development, and deployment of high-impact software and AI-driven solutions. This role requires a blend of hands-on technical leadership, strategic architecture oversight, and people management, driving excellence across the full software lifecycle while embedding AI/ML capabilities into core products and platforms.

Key Responsibilities

Software Engineering Leadership

1. Establish and maintain robust processes and documentation across the Software Product Lifecycle, from inception to retirement.
2. Oversee the end-to-end application lifecycle-including requirements analysis, system design, development, testing, deployment, release, and post-launch support.
3. Take ownership of critical technical components: UI/UX design, business logic, data access layers, external API integrations, and database schema design.
4. Evaluate and recommend tools, frameworks, and methodologies to ensure product quality, scalability, and maintainability.
5. Deliver production-ready software by writing clean, efficient, and well-documented code.
6. Mentor and coach engineering teams in: Writing comprehensive unit and UI tests, troubleshooting, debugging, and performance optimisation, designing intuitive user interfaces and developing real-time and batch data processing systems
7. Collaborate with Sales, Product, and Project teams to scope, prioritise, and deliver new features.
8. Ensure both new and legacy systems adhere to quality, security, and compliance standards.
9. Proactively research and propose new software products, tools, and architectural improvements.
10. Maintain thorough technical documentation for sustainability and knowledge transfer.
11. Keep abreast of emerging technologies and industry best practices.
12. Recruit top talent and design individualised training and career development plans.

AI/ML Strategy & Execution

1. Design, develop, and optimise AI models, leveraging both open-source and proprietary resources.
2. Build scalable AI solutions using cloud platforms (AWS/Azure/GCP), data pipelines, and modern databases.
3. Implement and deploy NLP, LLMs, and other AI frameworks for real-world use cases.
4. Architect and deliver RESTful APIs to expose AI capabilities to internal/external systems.
5. Apply Retrieval-Augmented Generation (RAG) to improve relevance, accuracy, and trustworthiness of AI outputs.
6. Practise advanced prompt engineering, including instruction tuning, templating, and fine-tuning strategies.
7. Embed AI development within CI/CD pipelines, enforcing test-driven development and reproducible workflows.
8. Conduct rigorous evaluation of models using appropriate metrics, benchmarks, and validation protocols.
9. Continuously monitor advancements in AI/ML research and translate them into business value.
10. Partner with stakeholders to convert business challenges into AI-powered solutions.
11. Ensure seamless integration of AI components with existing software ecosystems.

Requirements

Experience & Technical Background

• Bachelor's degree in Computer Science, Engineering, or a related field (Master's preferred).
• 10+ years of hands-on software development and leadership experience.
• Proven track record as a Team Lead or Tech Lead, managing end-to-end software projects.
• Strong domain experience in cybersecurity (especially European standards), IP telephony, and data networking, ideally in a project or technical management capacity.
• Demonstrated portfolio of released mobile/web applications (App Store, Play Store, or enterprise deployments).
• Proficiency in multiple programming languages: Essential: SQL, Python, Strongly preferred: Node.js, TypeScript, C#, JavaMobile: Swift, Objective-C, Java (Android), plus hybrid frameworks (e.g., React Native, Flutter)
• Expertise in: SDLC & Agile methodologies, data modelling, and relational (RDBMS) & NoSQL database design, REST/SOAP API integration, Cloud-based AI/ML deployment (AWS/Azure/GCP) (a plus), ML frameworks (TensorFlow, PyTorch) (a plus)

AI/ML Competencies

• Solid foundation in machine learning (supervised/unsupervised learning, neural networks, optimisation, evaluation metrics).
• Experience with data preprocessing, feature engineering, and SQL for AI pipelines.
• Deep understanding of algorithms, data structures, and their role in scalable ML systems.
• Familiarity with MLOps practices, including CI/CD for AI, model versioning, and monitoring.

Soft Skills & Attributes

• Exceptional verbal and written communication, able to explain technical concepts to non-technical audiences.
• Strong organisational, multitasking, and prioritisation skills.
• Self-motivated, independent, and able to thrive with minimal supervision.
• Collaborative team player who can inspire, mentor, and align engineers toward shared goals.
• Adaptable under pressure, with a bias for action in fast-paced environments.
• Committed to lifelong learning and technology innovation.
• Willingness to travel as required.