Applied AI Engineer

About the role

Validata, which helps banks, corporates and financial institutions with quality engineering, data migration, reconciliation and payment transformation, is hiring an Applied AI Engineer. You will work closely with Product to turn customer requirements into technical priorities, owning model selection, agent architecture, RAG pipelines and deployment. You will lead a small engineering team in building LLM systems, reusable APIs and agentic workflows that automate complex financial tasks.

Key responsibilities

• Define and drive the agentic AI roadmap into technical designs and delivery plans
• Lead agile innovation cycles from ideation through proof of concept, MVP and go-to-market
• Build multi-agent systems, including role definition, tool integration, planning, memory and workflow orchestration
• Build and optimise RAG pipelines and semantic search using vector databases, including indexing, retrieval policies and evaluation
• Integrate agent tools (MCP) and APIs with external services, databases and internal systems, with error handling and output validation
• Design evaluation frameworks for agent performance and apply prompt and context engineering with guardrails against hallucinations
• Deploy scalable AI services to the cloud with observability and monitoring, and build microservices architectures with tracing and metrics
• Partner with product teams to deliver human AI interfaces (voice, chat) and use data analysis to improve agents
• Set engineering standards through code reviews, testing, documentation and reusable components, and mentor team members

Required profile

• Degree in Computer Science, Data Science, Machine Learning or a related field, or equivalent practical experience; MSc or PhD a plus
• Proven experience building and deploying production AI/ML applications in FinTech, banking, payments or financial services
• Technical leadership, mentoring and coordination experience
• Strong written and verbal English, able to explain technical concepts to engineering and business stakeholders

Required skills

• Python and ML frameworks such as PyTorch, TensorFlow or scikit-learn
• Building LLM applications with agentic frameworks and generative models
• RAG pipelines, semantic search and vector database integrations
• Cloud platforms (AWS, Azure or GCP) and containerisation (Docker, Kubernetes)
• APIs, microservices and integrations; prompt optimisation and evaluation suite design
• Desirable: fine-tuning with LoRA, QLoRA or DoRA, quantisation and distillation, and Claude certifications

What we offer

• Competitive salary plus holiday and vacation allowance
• Flexible working: remote, hybrid or onsite, with flexible schedule and setup
• Continuous learning, mentorship and growth opportunities
• A supportive, collaborative team on high-profile banking projects