Johannesburg, South Africa | Posted on 05/13/2026 Weare seeking a Technical Lead Developer with AI project experience Senior AISoftware to consult and contribute deep technical expertise across modernsoftware engineering and AI-assisted development inside a regulated bankingenvironment. You will set the engineering standard for how AI coding assistantssuch as Claude Code and frameworks are used safely and effectively across delivery teams. Thisorganisation has a defined plan for how AI will be embedded into its softwaredelivery practice; the mandate is set, the priority use cases are agreed, andexecutive sponsorship is in place. What is needed now is hands-on engineeringleadership to turn that plan into working, production-grade systems. Thisis a hands-on role. You will spend the majority of your time writing code,reviewing code, and shaping technical implementation. You will be expected toset the bar for quality, security, and operational readiness, and to lift theengineers around you through direct technical mentorship rather than throughprocess or strategy. Requirements Hands-On Software Engineering Design,build, and ship production software across the AI delivery stack backendservices, APIs, and integration layers to a banking-grade standard of quality,observability, and resilience. Setthe technical pattern through reference implementations that other engineerscan extend, including service scaffolding, integration adapters, and agentorchestration components. Leadcode review across the team. Hold the line on testing discipline, secure codingpractice, error handling, performance, and maintainabilityand coach engineers through the reasoningbehind each call. Ownnon-functional engineering: logging, tracing, metrics, secrets management,dependency hygiene, and CI/CD pipeline quality. Make the path of leastresistance the correct path. Drivetechnical refactoring and modernisation of existing services where AI-assisteddelivery exposes structural debt that limits velocity or safety. AI-Assisted Software Engineering UseClaude Code and equivalent AI coding assistants as a daily engineering tool.Build the prompts, sub-agents, slash commands, hooks, and workflow conventionsthat make them effective on real banking codebases. PairAI-assisted delivery with the engineering discipline a regulated environmentdemandsexplicit human-in-the-loopcheckpoints, deterministic test gates, traceable change history, and clearseparation between AI-generated and human-authored code where audit requiresit. Buildinternal tooling that wraps AI coding assistants for banking use:codebase-scoped configurations, redaction layers for sensitive data, repo-awareprompts, and review automation that enforces the team's quality bar. Embedding AI-Assisted Delivery in Existing Engineering Squads Workdirectly inside existing engineering squads as a hands-on technicalcontributorpairing on real stories,writing code alongside their engineers, and integrating AI tools into theiractual development workflow rather than running it as a separate programme. Buildthe reusable engineering assets that make adoption stick: repo templates, agentconfigurations, prompt libraries Runtechnical workshops and pairing sessions with squad engineers. Identifythe engineering tasks where AI assistance has the highest returncode review, test generation, documentation,refactoring, integration scaffolding, log analysisand build the tooling and prompts that makethose wins repeatable. Engineering Guardrails for AI Tooling in a Banking Environment Defineand implement the engineering controls that govern AI coding assistant usage:which codebases and data are in scope, what AI-generated output requires humanreview before merge, and how AI-assisted commits are recorded for traceability. Buildthe technical enforcement of those controlsrepository configuration, branch protection, CI checks, prompt logging,and telemetryrather than relying onpolicy alone. Partnerwith Security, Risk, and Compliance engineering counterparts to ensure AItooling integrates cleanly with existing controls around secrets, dataclassification, change management, and SDLC evidence. Technical Mentorship Raisethe engineering capability of the team through direct technical mentorshippairing, code review, design review, andworked examplesnot through abstractguidance. Authorthe internal technical playbooks, reference implementations, and engineeringstandards for AI-assisted development inside the organisation, and keep themcurrent as the tooling evolves. Bethe engineer other engineers come to when something is genuinely hard. Hold thebar on technical quality without becoming a bottleneck. Yourexpertise: 8+years of hands-on software engineering experience, with a meaningful portion atLead or staff level shipping production systems that other engineers depend on. Deepproficiency in at least one modern backend language Java on the backend andAngular or React on the frontend. Other languages include Python,TypeScript/Node.js, Go - ideally the ability to operate effectively in apolyglot codebase. Strongapplied experience with API design, distributed systems, message-drivenarchitectures, and integration with enterprise systems of record. Demonstrableexperience designing for security, observability, and operational readiness inproductionnot as an afterthought. Solidcommand of testing discipline across unit, integration, contract, andend-to-end levels, and of CI/CD pipelines that enforce it. Experiencebuilding software inside a regulated environment, or comparable evidence ofworking under audit, change control, and data protection constraints. Ways of Working Ableto translate complex AI engineering decisions into clear, plain technicalwriting for engineering leads, security partners, and audit-facingstakeholders. Pragmaticunder ambiguity. Able to make defensible technical calls without waiting forperfect information, and willing to revisit them when evidence changes. Holdsthe engineering bar without becoming a blocker. Decisive in code review,generous in technical mentorship. #J-18808-Ljbffr