About the role
We're looking for an AI Engineer who sits comfortably at the intersection of data science, machine learning, and Generative AI.
You'll work on real-world AI solutions, from classical ML models to LLM-powered applications, and take them all the way from idea to production. This is not a research-only role, and not a pure software role either: it's about applied AI that delivers business impact.
You'll collaborate closely with data, engineering, and business stakeholders, and help shape how AI is used responsibly and effectively.
What you'll do
* Design, build, and evaluate machine learning models (classification, regression, forecasting, NLP, etc.)
* Develop Generative AI solutions (LLMs, RAG pipelines, prompt engineering, agents)
* Translate business problems into AI use cases with measurable impact
* Prepare, explore, and model data using strong data science foundations
* Build production-ready AI systems (APIs, pipelines, monitoring, retraining)
* Work with cloud and MLOps tooling to deploy and maintain models
* Communicate clearly with both technical and non-technical stakeholders
* Stay up to date with evolving AI and GenAI best practices
What you bring
* Strong foundation in data science & machine learning
* Hands-on experience with Python and ML libraries (e.g. scikit-learn, PyTorch, TensorFlow)
* Experience with or strong interest in Generative AI / LLMs (e.g. RAG, embeddings, prompt engineering)
* Solid software engineering mindset (clean code, version control, testing)
* Familiarity with cloud platforms (Azure, AWS, or GCP) and deployment patterns
* Comfort working across the full AI lifecycle: data → model → production
* Curious, pragmatic, and impact-driven mindset
Nice to have
* Experience with MLOps (CI/CD, MLflow, feature stores, monitoring)
* Experience with LLM orchestration frameworks (e.g. LangChain, LlamaIndex)
* Experience in regulated or enterprise environments
* Consulting or client-facing experience
Why join us
* Work on real AI solutions, not demos or slideware
* High ownership and autonomy
* Exposure to both classical ML and cutting-edge GenAI
* Strong focus on quality, responsibility, and long-term impact
* Opportunity to grow into technical lead or solution architect roles