Tech Lead Engineering - AI & Automation

TECH LEAD ENGINEERING – AI & AUTOMATION Purpose of the Role The Tech Lead Engineering – AI and Automation is the technical lead responsible for designing, developing and implementing AI and automation solutions that drive business efficiency, improve customer and employee experiences, and enhance operational productivity. Leading a team of engineers, this individual will translate AI and automation opportunities into secure, scalable and production-ready solutions that reduce manual processes and deliver measurable business value. The role will establish engineering standards, reference architectures and reusable frameworks for Generative AI, AI agents, intelligent document processing and process automation. The Tech Lead will remain hands-on, providing technical guidance, designing solutions, reviewing code, mentoring engineers and driving initiatives from proof of concept through to full-scale production. Working closely with business stakeholders, product teams, enterprise architecture, data, risk and technology teams, the role will help identify and prioritise AI and automation opportunities while ensuring solutions meet security, governance, data privacy and regulatory requirements. Ultimately, the Tech Lead Engineering – AI and Automation will play a key role in building the technical capability, platforms and engineering culture required to enable the successful adoption of AI and automation across the organisation. Key Responsibilities Lead, coach and develop a team of AI, automation and software engineers, including recruitment, performance management and skills development. Define and maintain engineering standards, reference architectures and reusable components for AI and automation solutions. Design and develop solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents and integrations with enterprise systems and APIs. Drive intelligent automation of business processes, including workflow automation, RPA and intelligent document processing. Take solutions from proof of concept to production, ensuring they are secure, scalable, observable and supportable. Establish MLOps/LLMOps practices, including CI/CD, model and prompt versioning, automated evaluation, monitoring and cost tracking. Implement responsible AI controls, including human-in-the-loop design, guardrails, accuracy and bias testing, explainability and audit trails. Ensure compliance with AI governance frameworks, POPIA and relevant regulatory requirements. Partner with business units and product owners to identify, assess and prioritise AI and automation opportunities based on business value, feasibility and risk. Collaborate with enterprise architecture, data, security and infrastructure teams to ensure seamless integration with core systems and cloud platforms. Identify opportunities to leverage shared platforms, reusable components and emerging technologies. Evaluate AI and automation tools and platforms, and manage relationships with technology vendors and partners. Monitor and report on delivery progress, solution adoption and business benefits realised, including productivity improvements, reduced turnaround times and cost savings. Stay informed of emerging AI and automation trends to influence technology strategy and innovation. Build AI literacy and engineering capability through knowledge sharing, technical demonstrations, mentoring and communities of practice. Overview of Role Responsibilities (Level 1–13) Enterprise Architecture – Level 6: Adaptation Maintain and update established enterprise architecture artefacts, ensuring alignment with organisational technology standards and architectural principles. Enterprise Infrastructure Modernisation – Level 6: Adaptation Deliver digital solution architecture services, preparing conceptual and logical architecture diagrams to guide technical direction and document future-state architectures. Business Requirements Identification – Level 6: Adaptation Gather and analyse business requirements through interviews, document analysis, workshops and workflow assessments, translating requirements into clearly defined user roles, objectives and functional needs. Analysis of As-Is and To-Be Processes – Level 6: Adaptation Document existing and future-state processes, identifying the changes required to achieve the desired business capabilities and operational improvements. Technical Developments Recommendation – Level 6: Adaptation Evaluate and recommend technical developments that improve the quality, efficiency and effectiveness of technology systems, processes, infrastructure and platforms. Minimum Requirements Degree in Computer Science, Engineering, Information Systems or a related field is advantageous. 8+ years of software engineering experience, including at least 3 years in a Technical Lead or Team Lead role. Proven experience delivering AI/ML or Generative AI solutions into production environments. Demonstrable experience delivering process automation solutions, including RPA, workflow automation or low-code platforms. Experience working with major cloud platforms such as Microsoft Azure or AWS. Relevant cloud certification (Azure or AWS) is advantageous. Financial services or insurance industry experience is advantageous. Technical Competencies Strong software engineering skills in Python and at least one of C#, Java or TypeScript, including API design and microservices architecture. Hands-on experience with LLMs and Generative AI, including prompt engineering, RAG, vector databases, agent orchestration and evaluation frameworks. Experience with automation and orchestration platforms such as UiPath, Microsoft Power Automate or similar technologies. Cloud-native development and AI services on major cloud platforms, including containers, DevOps and CI/CD. Strong understanding of data engineering fundamentals, including data pipelines, data quality and structured and unstructured data. Knowledge of AI security and governance, including identity and access management, data protection, model risk and POPIA compliance. Strong solution architecture and design capabilities, with the ability to assess build, buy and reuse opportunities. Proven leadership experience managing cross-functional engineering teams and influencing senior stakeholders. Excellent communication, problem-solving and stakeholder management skills.