Full Stack Developer - 2535
First seen by Alion on Nov 6, 2026.
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
Duties & Responsibilities- 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
- 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.
Package & Remuneration- 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