Join our brand-new AI & R&D team at the ground floor, shaping technical direction and building scalable, production‑grade machine learning and AI systems.
Reporting to the Head of AI & R&D, you will play a key role in designing, implementing, and validating advanced ML solutions across agentic LLMs, Retrieval‑Augmented Generation (RAG), and fine‑tuning foundation models.
This role blends hands‑on engineering with experimentation, moving from proof‑of‑concepts to high‑quality prototypes that pave the way for full‑scale production deployment.
Design, implement, and iterate on ML and AI initiatives from ideation through prototype.
Build, train, and evaluate LLM-based systems, including RAG pipelines, fine-tuning, and agentic workflows.
Develop clean, well-structured, and reusable Python code and ML components for experimentation.
Collaborate closely with AI/ML Engineers and Software Developers to transition prototypes toward production.
Create and maintain evaluation frameworks, benchmarking methods, and experiment tracking.
Support data preparation, feature engineering, and data quality processes.
Stay current with research across LLMs, multi-agent systems, embeddings, and applied generative AI.
Provide technical mentorship and contribute to shaping engineering standards and best practices.
Communicate technical findings clearly to both technical and non-technical audiences.

Requirements

Strong proficiency in Python and modern ML/AI libraries (PyTorch, TensorFlow, Hugging Face).
Hands-on experience with LLMs, RAG pipelines, and fine-tuning foundation models.
Solid understanding of core ML engineering: NLP, embeddings, supervised learning, deep learning architectures.
Experience designing and iterating on AI systems, measuring retrieval and response quality.
Familiarity with vector databases, prompt engineering, and evaluation metrics
Exposure to cloud platforms (GCP / Azure / AWS) and MLOps practices (CI/CD, testing, versioning).
Ability to write production-ready code following best practices.
Excellent communication skills for breaking down technical concepts.
Experience in Generative AI beyond LLMs (Nice-to-Have).
Academic research or publications (Nice-to-Have).
Experience with Big Data tooling or large-scale distributed systems (Nice-to-Have).

Benefits

Be a pioneer: Influence the foundations of a new AI function from day one.
Flexibility: Hybrid working model with flexible working hours.
Global exposure: Work with teams and stakeholders across multiple global offices.
Cutting-edge tech: Experiment with the latest AI/LLM advancements and emerging technologies.
Collaboration: Partner with diverse, talented teams and contribute to impactful initiatives.
Health insurance
Retirement plans
Paid time off
Professional development

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonPyTorchTensorFlowHugging FaceLLMsRAG pipelinesfine-tuningNLPembeddingsdeep learning architectures

Soft Skills

communication skillstechnical mentorship