Lead AI Engineer; LLMs & Agents

Are you passionate about leveraging AI to transform global industries? Do you have a proven track record of working with LLMs and building agentic solutions?

Join Elixirr Digital as a Lead AI Engineer, where you will drive innovation and help shape the future of intelligent systems for clients across industries like banking, insurance, healthcare, telecommunications, consumer goods, and more. We're looking for proactive self-starters who are constantly reading research, applying it, and using AI to improve their processes, fully embracing the technology.

At Elixirr Digital, we've been on the cutting edge of generative AI from the beginning, developing our own platforms and deploying transformative solutions to global clients for several years. Our expertise spans working with frontier and open-source models across providers including Anthropic, OpenAI, Google, and Meta, and leading frameworks like Lang Chain, Google ADK, and Auto Gen to build innovative, agentic solutions built on open standards like the Model Context Protocol (MCP).

From underwriting agents that empower financial professionals to knowledge agents streamlining customer workflows, our solutions are revolutionizing industries by embedding AI at their core.

If you're excited to work at the forefront of generative AI, helping enterprises transform from the inside out, we want to hear from you. Let's build the future of AI together!

Candidates applying, kindly note that we are also considering the wider surrounding areas of Johannesburg and Cape Town for this position.

What will you be doing as a Lead AI Engineer at Elixirr Digital?

• Design and Implement Solutions: Architect, design, and develop advanced AI systems with a focus on LLMs and agentic solutions, including multi-agent orchestration, tool design, durable execution, and memory-augmented workflows that transform enterprise processes.
• Lead and Collaborate: Lead teams, mentor junior engineers, and collaborate with cross-functional experts to deliver AI-driven innovations.
• Drive Innovation: Work on a mix of internal R&D projects and enterprise-level solutions, building and optimizing tools for both internal and client use.
• Deploy at Scale: Develop and maintain production-ready AI solutions, ensuring scalability, performance, reliability, and robust observability through tracing and evaluation frameworks.
• End-to-End Lifecycle: Own and contribute to the entire AI development lifecycle, from research and prototyping to deployment, monitoring, and continuous improvement through production feedback loops.
• Deliver Tangible Outcomes: Help global enterprises achieve measurable impact through solutions like underwriting agents, airport assistants, pharmacy workflow optimizers, and customer due diligence systems.

Competencies and skillset we expect you to have to successfully perform your job:  

• Have Deep Experience with LLMs and Agents: Hands-on experience building and deploying production agentic systems, including multi-agent architectures, tool/function calling design, and agent memory models. Familiarity with orchestration frameworks such as Lang Chain, Google ADK, or Auto Gen, and working knowledge of open standards like the Model Context Protocol (MCP).
• Bring Technical Expertise: Solid background in programming and past experience in machine learning, with a deep understanding of at least one area such as deep learning, reinforcement learning, fine-tuning, or model development.
• Exercise Strong Model Judgment: Ability to evaluate and select across frontier and open-source models from providers including Anthropic, OpenAI, Google, and Meta, and articulate the tradeoffs between hosted inference, self-hosted deployment, fine-tuning, RAG, and agentic routing for a given use case.
• Think Strategically: Ability to design and lead AI solutions while also contributing directly to development. Excellent problem-solving, analytical, and critical thinking skills, with a proficiency in applying mathematical principles to analyze, model, and solve complex problems.
• Own Quality and Observability: Experience designing LLM evaluation frameworks, implementing tracing and observability tooling (e.g. Langfuse,…