Generative AI Technical Lead
iLaunch
Date: 15 hours ago
City: Johannesburg, Gauteng
Contract type: Full time
GenAI Delivery: Build and deploy high-impact features, including prompts, prototypes, model integrations, and MLOps-lite workflows.
Team Leadership: Mentor 3–5 engineers, lead code/design reviews, and uphold secure, test-driven practices.
Architecture: Drive squad design sessions and contribute to GenAI architecture standards.
Risk & Compliance: Classify GenAI risks and implement safeguards for privacy, bias, and reliability.
Monitoring: Define SLOs and set up monitoring for cost, latency, and accuracy.
Collaboration: Work with cross-functional teams to refine requirements, ship safely, and share learnings.
IT-related tertiary qualification
4 years’ experience in software development or machine learning, with at least 1 year building and running GenAI or LLM-based solutions in production, including safety features and guardrails.
Practical knowledge of evaluating LLMs, covering RAG performance (e.g. recall/precision), hallucination detection, bias/toxicity assessment, and benchmarking for cost and latency.
Proven track record of technical leadership—mentoring, reviewing code, leading architecture decisions, and supporting sprint planning.
Hands-on expertise with:
Python and leading GenAI frameworks (such as OpenAI, Hugging Face, Anthropic, or Gemini)
Vector search tools like Pinecone, Weaviate, or FAISS within RAG setups
Front-end and back-end technologies (React, JavaScript/HTML, REST/gRPC APIs), containerization (Docker), and Git version control
Strong systems thinking with a design approach rooted in prompt engineering and GenAI-first principles.
Between 3 - 5 Years
Team Leadership: Mentor 3–5 engineers, lead code/design reviews, and uphold secure, test-driven practices.
Architecture: Drive squad design sessions and contribute to GenAI architecture standards.
Risk & Compliance: Classify GenAI risks and implement safeguards for privacy, bias, and reliability.
Monitoring: Define SLOs and set up monitoring for cost, latency, and accuracy.
Collaboration: Work with cross-functional teams to refine requirements, ship safely, and share learnings.
IT-related tertiary qualification
4 years’ experience in software development or machine learning, with at least 1 year building and running GenAI or LLM-based solutions in production, including safety features and guardrails.
Practical knowledge of evaluating LLMs, covering RAG performance (e.g. recall/precision), hallucination detection, bias/toxicity assessment, and benchmarking for cost and latency.
Proven track record of technical leadership—mentoring, reviewing code, leading architecture decisions, and supporting sprint planning.
Hands-on expertise with:
Python and leading GenAI frameworks (such as OpenAI, Hugging Face, Anthropic, or Gemini)
Vector search tools like Pinecone, Weaviate, or FAISS within RAG setups
Front-end and back-end technologies (React, JavaScript/HTML, REST/gRPC APIs), containerization (Docker), and Git version control
Strong systems thinking with a design approach rooted in prompt engineering and GenAI-first principles.
Between 3 - 5 Years
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