AI Engineer

🌍 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