Jobs at Badger Holdings (Pty) Ltd

About the role

• You’ll turn powerful AI systems into real, usable products; the kind customers and teams rely on every day. At ARC AI, we’re building intelligent agents that operate in a regulated insurance environment, and your role is to make them work in the real world.
• This means designing and building the interfaces, APIs, and product layer that sit between AI and people. You’ll shape how users interact with AI: from chat interfaces and workflows to human-in-the-loop review systems that keep humans in control of agent decisions before they reach customers. Ensuring everything works smoothly, even when the AI doesn’t.
• You’ll own the full stack: frontend experiences that handle streaming responses and unpredictable outputs, backend services that connect AI to core systems, and the pipelines that ship it all into production.
• You’ll also build the observability and feedback loops that help us continuously improve how these agents perform in production, not just in evaluation, but in the real product.
• This is a hands-on, high-ownership role where what you build is the product, seen and used by customers and leadership alike.

Requirements

About you

• You’re an experienced full-stack engineer who has already shipped AI-powered products into production.
• You understand that building for AI is different. You’ve worked with non-deterministic systems, handled variable latency, and designed experiences that gracefully manage failure, partial responses, and uncertainty.

You’re confident across the stack:

• Modern frontend frameworks (React or similar)
• Backend/API development (Node.js, Python, or similar)
• REST/GraphQL APIs at scale
• Secure, compliant data handling in regulated environments
• You’ve built systems where reliability, security, and control matter, including authentication, validation, and enforcing boundaries at the API layer (not relying on the model to behave).
• You’re also comfortable owning the full lifecycle: from design and development to CI/CD and deployment.
• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related field, or equivalent practical experience demonstrating shipped production AI work
• 5+ years’ experience in full-stack software engineering roles
• Demonstrated experience shipping user-facing AI or LLM-powered products into production environments

Bonus points

We’d love to hear from you if you’ve also worked with:

• Conversational interfaces, Azure, or LLM observability tools
• Complex system integrations like CRM platforms
• Training or certifications in secure software development or data protection (e.g. GDPR/POPIA awareness)

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About the Role

ARC AI builds AI agents as products: systems that operate autonomously across business functions, handle real customer interactions, and deliver measurable outcomes in a regulated insurance environment. This is not an experimentation function. We ship.

We are looking for a Senior Software Engineer who has built and operated AI agent systems in production. Someone who knows what it takes to move an agent from prototype to live deployment, and who understands that evaluation, not iteration speed, separates reliable AI products from expensive demos.

Requirements

What You Will Own

• You will design, build, and operate the AI agent systems that power ARC AI products. This means owning the full arc: architecture, evaluation pipelines, production reliability, guardrails, and the backend integrations that connect agents to real enterprise data.
• You will set the engineering standards the rest of the team follows. Evaluation is a first-class responsibility here, not a QA afterthought. Securing an agent means architecting enforcement outside the model; defences sit at the execution boundary, not inside the system prompt. Compliance with POPIA and GDPR is an architectural decision you make at the start, not a checkpoint at the end.
• The architectural decisions you make in this role will be the ones every subsequent engineer builds on.

What We Require

Non-negotiable

• Demonstrated experience building and shipping AI agent systems in production: not demos, not internal tools that never went live
• Ownership of an evaluation pipeline for a production AI system: you defined the metrics, built the framework, and used it to make deployment decisions
• Experience debugging production AI failures: you have traced a silent agent degradation to its root cause in a live system
• Proficiency in Python and LLM frameworks (LangChain, LlamaIndex, or equivalent)
• RAG architecture: retrieval pipeline design, vector database implementation, chunking and embedding strategy
• API design and backend integration at production scale
• Secure tool access patterns for agentic systems: you know where LLM reasoning ends and deterministic enforcement must begin, and you have built the boundary between them
• Experience implementing guardrails: input validation, output filtering, and execution-layer prompt injection defence
• CI/CD for agentic systems: you have implemented progressive delivery pipelines for agents, including instrumentation of tool invocations and decision points, staging with regression benchmarks, and canary deployment to detect behavioural drift before full rollout

 Strong Advantage

• Experience in regulated financial services, insurance, or healthcare environments
• Familiarity with graph-based agentic orchestration frameworks (LangGraph or equivalent), particularly durable execution and human-in-the-loop checkpointing in regulated environments
• Familiarity with Azure AI tooling and services
• MLOps practices: monitoring, observability, cost management for inference workloads
• CRM and enterprise system integration

Educational Requirements

• Bachelor's degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, Data Science, or a related field
• OR equivalent practical industry experience building and deploying production-grade AI systems