
AI Engineer
Vie hiiri rivin päälle tai napauta riviä nähdäksesi täydelliset tilastot (EUR / kk tässä kaaviossa).
Palkka-analyysi
Palkkatarjous sijoittuu vertailutasoon "Markkinoiden keskiarvo: AI Engineer" nähden: palkkahaarukan keskipiste on noin 92 % vertailutason alapuolella. Kokonaisuutena tarjous asettuu markkinahaarukan (€10 128–€14 468) alapuolelle. Tarjotun palkkavälin leveys on lähellä markkinan tasoa. Vertailu perustuu 2 vastaavaan ilmoitukseen.
| Markkinat | Alaraja (25. persentiili) | Mediaani | Yläraja (75. persentiili) |
|---|---|---|---|
| Markkinoiden keskiarvo: AI Engineer | €10 128/kuukaudessa | €12 298/kuukaudessa | €14 468/kuukaudessa |
| Tietojemme mukainen palkka — ei mainittu ilmoituksessa (Keskitaso) | €844/kuukaudessa | €1 025/kuukaudessa | €1 206/kuukaudessa |
Pretoria AI Engineer About the Role We’re looking for a skilled AI Engineer to help us fine-tune, deploy, and maintain private AI models tailored to our business needs. You’ll be working with leading foundation models and custom datasets to deliver scalable, secure, and high-performing AI solutions—without reinventing the wheel. What You’ll Do 🧠 Model Fine-Tuning & Adaptation Fine-tune pre-trained private AI models (e.g. LLMs, vision models) for specific business use cases. Host and manage local LLMs and app hosting Work with proprietary or internal datasets to adapt models for high relevance and accuracy. Evaluate model performance and improve outputs through prompt engineering or targeted retraining. 🧹 Data Preparation Collect, clean, and prepare datasets for training, tuning, and evaluation. Collaborate with data teams to ensure high-quality inputs and labeling consistency. ⚙️ Deployment & Operations Deploy models in secure, scalable production environments using Docker, Kubernetes, Linux and cloud infrastructure (AWS, GCP, or Azure). Monitor model performance, reliability, and drift; implement updates and improvements as needed. 🛠️ Maintenance & Optimisation Maintain and update deployed models to ensure continued alignment with business goals. Optimize model latency, cost, and accuracy based on real-world usage data. 🤝 Collaboration & Support Work with product and engineering teams to integrate models into applications. Support internal teams with prompt design, model usage, and troubleshooting. ⚖️ Responsible AI Practices Apply principles of ethical AI development, including privacy, security, and bias mitigation. Ensure compliance with internal and external AI governance policies. What You’ll Need 1+ years’ experience in AI/ML/GI engineering, with a focus on model fine-tuning and deployment. Strong experience with PHP, Node, React & Python and libraries like Transformers, LangChain, or Hugging Face. Familiarity with model evaluation techniques and metrics. Experience deploying AI models in production using tools like Docker, Kubernetes, and cloud services (AWS, GCP, or Azure). Solid understanding of LLMs or other foundation models and how to work with them effectively. Strong analytical, problem-solving, and communication skills. Nice to Have Experience with vector databases (e.g. FAISS, Weaviate, Pinecone) or RAG pipelines. Knowledge of secure and private model hosting (eg Ollama). Certifications in ML, cloud, or AI-related fields. Exposure to tools like MLflow, Weights & Biases, or Ray. Orchestration automation like n8n Why Join Us? You’ll work on impactful AI solutions without the burden of building from scratch—just smart adaptation, deployment, and ongoing improvement. Help shape how AI is applied responsibly and effectively in the real world.
Työn tiedot
Vastuualueet
- Mallien hienosäätö ja mukauttaminen
- Datan valmistelu
- Toteutus ja operointi
- Ylläpito ja optimointi
- Yhteistyö ja tuki
- Vastuullinen tekoäly -käytännöt
Vaatimukset
- 1+ vuoden kokemus AI/ML/GI-insinöörityksestä, keskittyen mallien hienosäätöön ja käyttöönottoon.
- Vahva kokemus PHP:stä, Node:stä, React:stä & Pythonista sekä kirjastoista kuten Transformers, LangChain tai Hugging Face.
- Kokemus mallien arviointitekniikoista ja mittareista.
- Kokemus AI-mallien käyttöönotosta tuotannossa Dockerin, Kubernetesin ja pilvipalvelujen (AWS, GCP, tai Azure) avulla.
- Hyvä ymmärrys LLM- tai perusmallien kanssa työskentelystä.
Taidot ja teknologiat

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