{"id":4276490,"url":"https://alion.io/job/mediro-ict-recruitment-full-stack-developer-2535","title":"Full Stack Developer - 2535","company":{"id":4508007,"name":"Mediro ICT Recruitment","domain":"mediro-ict.co.za","url":"https://alion.io/company/mediro-ict-recruitment-2","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Backend","role_family":"Backend","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Midrand, South Africa"],"countries":["ZA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI 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focused on building the DVF AI platform capability by designing and implementing reusable AI platform services, APIs, libraries, templates and engineering patterns\n3-year renewable Contract - Gauteng\n\nDuties & Responsibilities\nEnable DVF AI use cases from discovery and prototype through production implementation\nEngineer agentic and GenAI solutions including agents, RAG capabilities, prompts, context packages, tool integrations, evaluations and guardrails\nDrive AI Native engineering by embedding AI into specification, development, testing, code review, documentation and operational workflows\nImplement Snowflake capabilities and engineer data structures and integration patterns\nEstablish production AI engineering standards for evaluation, monitoring, observability, security and production-readiness\nEnsure solutions address enterprise requirements for security, privacy, governance, information protection, auditability, resilience and operational support\nIdentify recurring requirements across use cases and convert them into shared DVF platform capabilities\nProvide technical leadership through architecture and design discussions, code reviews, and mentoring engineers\nSupport production deployment, monitoring, incident resolution and continuous improvement\n\nDesired Experience & Qualification\nDegree or diploma in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Data Engineering, Information Technology, or a related discipline\nTypically 7+ years of relevant engineering experience\nDemonstrable experience building and operating complex production systems\nExpert-level Python engineering capability\nDemonstrable production experience with Generative AI/ LLM solutions\nStrong data and integration engineering background\nPractical experience implementing AI-assisted or AI-native engineering practices\nExperience with Snowflake implementation or comparable enterprise cloud data platforms\nEvidence of taking AI solutions beyond prototypes into governed, production-ready implementations\nESSENTIAL SKILLS:\nAI Engineering\nExpert knowledge of LLMs and Generative AI application engineering.\nHands-on experience building production-grade LLM applications and AI agents.\nStrong understanding of Retrieval-Augmented Generation (RAG).\nPrompt and context engineering.\nAgent/tool integration and orchestration.\nStructured outputs and tool calling.\nModel and application evaluation.\nAI guardrails and human approval mechanisms.\nAI observability, reliability and production monitoring.\nResponsible and secure AI engineering.\nThe source role already identifies agents, RAG, prompt/context engineering, tool integration, evaluation,\nguardrails and human approval gates as core AI engineering capabilities.\nAI Native Engineering\nStrong practical capability using AI throughout the software engineering lifecycle.\nSpecification-driven development.\nAI-assisted implementation and refactoring.\nAI-assisted test generation and quality engineering.\nAI-supported code review and documentation.\nAbility to build reusable AI-enabled developer tooling and workflows.\nStrong understanding that AI-generated outputs still require accountable engineering review.\nThe reference specification explicitly expects responsible AI-assisted development across specification,\ncoding, review, testing, documentation and operations while retaining accountability for correctness and\nsecurity.\nSoftware & Platform Engineering\nExpert Python engineering.\nStrong API and microservice engineering.\nREST-based service integration.\nEvent-driven and asynchronous architecture.\nContainers and Kubernetes.\nCI/CD and automated testing.\nInfrastructure as Code.\nSecure software development.\nProduction observability and operational readiness.\nStrong architecture and systems-thinking capability.\nPython/Java, APIs, microservices, AWS/cloud-native engineering, Docker/Kubernetes/Terraform, automated\ntesting, CI/CD and production readiness are all represented in the reference Expert specification.\nSnowflake & Data Engineering\nStrong Snowflake engineering and implementation capability.\nDesign and implementation of data models suitable for AI and application consumption.\nAdvanced SQL.\nData ingestion and transformation.\nETL/ELT engineering.\nMetadata, lineage and data quality.\nSecure data access patterns.\nIntegration between Snowflake, AI services, APIs and enterprise applications.\nPerformance and cost-conscious data engineering.\n\nPackage & Remuneration\nFlexible working of 1960 hours in a 12-month period\nHigh Work-Life balance\nRemote / On-site work location flexibility\nModern, state-of-the-art offices\n\nInterested?\nMediro 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