Full Stack Developer - 2535

About the role

• An expert-level role focused on building the DVF AI platform capability by designing and implementing reusable AI platform services, APIs, libraries, templates and engineering patterns

3-year renewable Contract - Gauteng

• Enable DVF AI use cases from discovery and prototype through production implementation
• Engineer agentic and GenAI solutions including agents, RAG capabilities, prompts, context packages, tool integrations, evaluations and guardrails
• Drive AI Native engineering by embedding AI into specification, development, testing, code review, documentation and operational workflows
• Implement Snowflake capabilities and engineer data structures and integration patterns
• Establish production AI engineering standards for evaluation, monitoring, observability, security and production-readiness
• Ensure solutions address enterprise requirements for security, privacy, governance, information protection, auditability, resilience and operational support
• Identify recurring requirements across use cases and convert them into shared DVF platform capabilities
• Provide technical leadership through architecture and design discussions, code reviews, and mentoring engineers
• Support production deployment, monitoring, incident resolution and continuous improvement

What they're looking for

• Degree or diploma in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Data Engineering, Information Technology, or a related discipline
• Typically 7+ years of relevant engineering experience
• Demonstrable experience building and operating complex production systems
• Expert-level Python engineering capability
• Demonstrable production experience with Generative AI/ LLM solutions
• Strong data and integration engineering background
• Practical experience implementing AI-assisted or AI-native engineering practices
• Experience with Snowflake implementation or comparable enterprise cloud data platforms
• Evidence of taking AI solutions beyond prototypes into governed, production-ready implementations

ESSENTIAL SKILLS:

AI Engineering

• Expert knowledge of LLMs and Generative AI application engineering.
• Hands-on experience building production-grade LLM applications and AI agents.
• Strong understanding of Retrieval-Augmented Generation (RAG).
• Prompt and context engineering.
• Agent/tool integration and orchestration.
• Structured outputs and tool calling.
• Model and application evaluation.
• AI guardrails and human approval mechanisms.
• AI observability, reliability and production monitoring.
• Responsible and secure AI engineering.
• The source role already identifies agents, RAG, prompt/context engineering, tool integration, evaluation, guardrails and human approval gates as core AI engineering capabilities.
• AI Native Engineering
• Strong practical capability using AI throughout the software engineering lifecycle.
• Specification-driven development.
• AI-assisted implementation and refactoring.
• AI-assisted test generation and quality engineering.
• AI-supported code review and documentation.
• Ability to build reusable AI-enabled developer tooling and workflows.
• Strong understanding that AI-generated outputs still require accountable engineering review.
• The reference specification explicitly expects responsible AI-assisted development across specification, coding, review, testing, documentation and operations while retaining accountability for correctness and security.
• Software & Platform Engineering
• Expert Python engineering.
• Strong API and microservice engineering.
• REST-based service integration.
• Event-driven and asynchronous architecture.
• Containers and Kubernetes.
• CI/CD and automated testing.
• Infrastructure as Code.
• Secure software development.
• Production observability and operational readiness.
• Strong architecture and systems-thinking capability.
• Python/Java, APIs, microservices, AWS/cloud-native engineering, Docker/Kubernetes/Terraform, automated testing, CI/CD and production readiness are all represented in the reference Expert specification.
• Snowflake & Data Engineering
• Strong Snowflake engineering and implementation capability.
• Design and implementation of data models suitable for AI and application consumption.
• Advanced SQL.
• Data ingestion and transformation.
• ETL/ELT engineering.
• Metadata, lineage and data quality.
• Secure data access patterns.
• Integration between Snowflake, AI services, APIs and enterprise applications.
• Performance and cost-conscious data engineering.

What's on offer

• Flexible working of 1960 hours in a 12-month period
• High Work-Life balance
• Remote / On-site work location flexibility
• Modern, state-of-the-art offices