Senior Software Engineer, Applied AI – George (Onsite) at Datafin Recruitment

ENVIRONMENT:


Our client who builds AI agents as products, systems that operate autonomously across business functions, handle real customer interactions, and deliver measurable outcomes within a regulated insurance environment, is looking for a Senior Software Engineer, Applied AI. This is not an experimentation function. The focus is on shipping reliable AI products.
You will be part of a small, high-impact team building something from the ground up. There are no layers of separation, no handoffs to QA, DevOps, or external teams. You own what you build. Your work will directly shape how AI is experienced by real customers in a regulated environment. It is complex, meaningful, and visible.


DUTIES:


You will design, build, and operate the AI agent systems that power our client's 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.


REQUIREMENTS:

Non-negotiable:


Demonstrated experience building and shipping AI agent systems in production (not demos or 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, including tracing 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
We recognise that exceptional experience and demonstrated capability can be just as valuable as formal qualifications.


ATTRIBUTES:


You are someone who has built and operated AI agent systems in production and knows what it takes to move an agent from prototype to live deployment. You understand that evaluation, not iteration speed, separates reliable AI products from expensive demos.
You thrive in a highly autonomous, engineering-led environment. You are ready to take full ownership of what you build, from architecture through to operations. You are motivated by technically challenging problems at the intersection of AI, reliability, and compliance.
If you have built AI products that made it into users' hands and you are ready to take full ownership of what comes next, this is the role for you.