AI Engineer

We are looking for an experienced Senior Data & AI Engineer to lead the development of scalable data pipelines and operational AI solutions. In this role, you will work closely with our Business Information teams to deliver innovative solutions based on company, financial, and risk data. Key responsibilities include implementing LLM-powered applications, generative AI use cases, RAG frameworks, and AI agents, while ensuring compliance with security and responsible AI standards. The emphasis is on deploying and integrating AI solutions into enterprise environments rather than training models from the ground up. Strong professional English communication skills are required.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines (ETL/ELT), ensuring high data quality and reliability to support AI, analytics, and business intelligence initiatives.
• Build and deploy AI-powered solutions leveraging Large Language Models (LLMs) and Generative AI technologies (GPT, Claude, Gemini, and open-source models)
• Architect and implement Retrieval-Augmented Generation (RAG) solutions using vector databases to deliver accurate, context-aware AI applications.
• Develop intelligent AI agents and multi-agent systems, apply advanced prompt engineering, orchestration frameworks, and tooling such as LangChain, LlamaIndex, and Semantic Kernel.
• Integrate AI capabilities into enterprise applications through APIs and microservices and manage deployment and operations on cloud platforms such as AWS, Azure, or GCP.
• Identify, evaluate, and mitigate AI-related risks, including prompt injection, data leakage, and hallucinations, while implementing robust guardrails and Responsible AI best practices.
• Ensure compliance with data privacy regulations (GDPR) and emerging AI governance frameworks throughout the entire AI solution lifecycle.
• Leverage expertise in business information domains, including corporate, financial, and risk data, to design solutions that address real-world business challenges.
• Optimize AI workloads for performance, scalability, cost efficiency, latency, and token consumption in production environments.
• Collaborate closely with product managers, software engineers, data professionals, and business stakeholders to translate requirements into technical solutions, while documenting architecture, standards, and best practices.