🌍 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