Lead Generative AI Engineer

The Skills Mine; Cape Town; On-site; USD 48,000 - 72,000; Full time

Job summary

A leading company in Cape Town seeks an experienced Software/Machine Learning Engineer to develop and enhance Generative AI solutions. In this role, you will leverage your deep understanding of AI technologies and mentor engineers while delivering impactful features. Collaborative interactions with cross-functional teams ensure safe product delivery.

Qualifications

• 4+ years in software/machine learning engineering.
• 1+ year experience in Generative AI solutions.
• Experience mentoring engineers.

Responsibilities

• Deliver GenAI features via prompt engineering and model integrations.
• Coach engineers through pair programming.
• Run design and code reviews, ensuring best practices.

Skills

Python

Mentoring

Integrating vector databases

Prompt engineering

Code reviews

Tools

OpenAI SDK

Hugging Face Transformers

Job description

• 4+ years of professional experience in software or machine learning engineering, or demonstrable equivalent impact.
• At least 1 year of hands-on experience building and operating production-ready Generative AI / LLM solutions in live products, including the implementation of guardrails, hallucination reduction techniques, and safety mitigations.
• 1+ year of experience in mentoring or technical leadership, including responsibilities such as code reviews, architecture sign-off, and sprint planning / guidance.
• Proven experience developing in Python, along with proficiency in at least one major Generative AI SDK (e.g. OpenAI, Anthropic, Gemini, or Hugging Face Transformers).
• Strong background in integrating and tuning vector databases (such as Pinecone, Weaviate, or FAISS) to support retrieval-augmented generation (RAG) pipelines.

Responsibilities :

• Deliver high-impact GenAI features hands-on : prompt engineering, rapid prototypes, model integrations, and MLOps-lite pipelines.
• Coach 3-5 engineers through pair programming, goal-setting, and career growth.
• Run design and code reviews; uphold secure, well-documented, test-driven practices.
• Contribute to the AI Technical Committee reviewing new solution designs and reference architectures.
• Classify GenAI initiatives into low / medium / high risk (e.g., marketing copy vs. compliance automation).
• Shape mitigation plans covering model choice, data privacy, bias, latency, and hallucination.
• Define SLOs for latency, cost, and accuracy; instrument telemetry and alerts.
• Partner with Product Owners, Ops, and Risk to refine requirements and ship safely.