senior ai engineer

As an AI Engineer, you will be a key member of the AI & Innovation team, working closely with other engineers and the Head of AI. You will be responsible for designing, developing, and deploying AI models and pipelines, with a strong focus on Large Language Models (LLMs) and Generative AI. This is a hands-on role, ideal for someone passionate about applying state-of-the-art AI technologies to real-world enterprise problems in the financial services domain.

Key Responsibilities:

• Design and implement end-to-end ML/LLM pipelines (data preprocessing, embeddings, model integration, deployment).

• Develop and optimize RAG (Retrieval-Augmented Generation) pipelines using vector databases and embeddings.

• Work on document intelligence projects, including parsing, classification, and information extraction from unstructured text.

• Contribute to text-to-SQL and text-to-API solutions, enabling natural language queries over structured data and APIs.

• Collaborate with the team to build speech-to-text models (with a focus on Greek language datasets).

• Integrate models into enterprise applications using APIs, Docker, and cloud environments.

• Evaluate, monitor, and improve the performance of AI systems in production.

• Stay up to date with the latest trends in Generative AI, NLP, and ML research.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.

• 3–5 years of experience in Machine Learning, Data Science, or AI Engineering, with at least 1–2 years hands-on experience with LLMs/Generative AI.

• Strong programming skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, HuggingFace Transformers).

• Experience with LLM APIs (OpenAI, Anthropic, Azure OpenAI, HuggingFace).

• Hands-on experience with RAG pipelines, vector databases (e.g. FAISS, Pinecone, Chroma), and LangChain.

• Knowledge of prompt engineering, fine-tuning, and model evaluation.

• Experience deploying ML models into production using APIs, Docker, cloud platforms (Azure, AWS, or GCP).

• Familiarity with SQL and structured data processing.

• Strong problem-solving skills and ability to adapt AI solutions to new business domains (finance/banking experience is a plus).

Nice-to-Have:

• Experience with speech-to-text systems or multilingual NLP.

• Previous exposure to financial services/banking applications.

• Familiarity with MLOps tools (monitoring, CI/CD for ML, LangSmith, LangFuse).

What They Offer:

• Hybrid working model.

• Opportunity to work on cutting-edge AI projects with real-world impact in the financial services industry.

• Exposure to international clients and enterprise-grade solutions.

• A collaborative, innovation-driven environment with growth opportunities.