Head of AI Engineering
Description
Validata is growing, and we are looking for a highly skilled Head of AI Engineering to raise the bar on delivery and quality for mission-critical banking environments. You'll be at the forefront of building and optimising production-grade LLM systems, reusable APIs and Agentic AI workflows to automate complex financial tasks. This is a hands-on leadership role for someone who can balance strategy and execution—setting direction, enabling teams, and making sure we deliver measurable outcomes for customers.
Responsibilities
Define and drive Validata's Agentic AI roadmap, aligning AI innovation with measurable
business outcomesLead agile innovation cycles from ideation through proof of concept, MVP, and go-to-
marketArchitect multi-agent systems — covering role definition, tool integration, planning,
memory, and workflow orchestrationBuild and optimise RAG pipelines and semantic search using vector databases, including
indexing, retrieval policies, and evaluationIntegrate agent tools (MCP) and APIs to connect agents with external services, databases, and internal systems — with robust error handling and output validation
Design evaluation frameworks to systematically assess agent performance across key
operational metricsApply advanced prompt and context engineering and implement guardrails to reduce
hallucinationsDeploy scalable AI services to cloud infrastructure with full observability and monitoring
Build microservices-based architectures and instrument agents for tracing, metrics, and
continuous improvementPartner with stakeholders to shape requirements and deliver intuitive human-AI interfaces
(voice, chat)Use data analysis to inform agent capabilities, optimise retrieval, and drive decision-
makingMentor and upskill team members on agent frameworks, LLM best practices, and AI
safety
MSc or PhD in Computer Science, Data Science, Machine Learning, or a related field
Proven experience building and deploying AI/ML applications in FinTech, Banking & Payments, or financial services
Strong Python skills and proficiency with ML frameworks (PyTorch, TensorFlow, scikit-learn)
Hands-on experience with agentic frameworks
Experience with generative models
Familiarity with cloud platforms (AWS, Azure, GCP) and containerisation (Docker, Kubernetes)
Experience fine-tuning SLMs using LoRA, QLoRA, or DoRA; quantization and distillation are a plus
Familiarity with prompt optimisation frameworks and evaluation suite design
Strong communication skills — equally comfortable with engineers and executive stakeholders
Experience leading data science teams and fostering a culture of growth and shared learning
Strong leadership presence and communication skills with the ability to delegate responsibilities effectively unblock teams, and drive decisions
Excellent English communication skills: clear written and verbal communication
Ability to make decisions in a fast-paced, dynamic environment
Highly competitive salary, plus additional benefits and perks
Holiday and vacation allowance
A long-term challenging role with meaningful ownership and impact
Work in an agile environment on high-profile banking projects
Flexible working built around you — choose remote, hybrid, or onsite, with the freedom to adapt your schedule and setup to what helps you do your best work
Growth is a priority: we invest in your development with continuous learning and mentorship to build the skills that move your career forward
Join a supportive, collaborative team where knowledge is shared, wins are celebrated, and you’re set up to thrive
Equal Employment Opportunity Policy
Validata Group is an equal opportunity employer. We provide equal employment opportunities to all
applicants and employees, and we’re committed to an inclusive workplace where everyone is treated with
dignity and respect and diverse perspectives are truly valued.
If this sounds interesting and you think we might “click”, we’d be happy to hear from you!
Skills
- Agentic AI
- Agile
- AI
- API
- AWS
- Azure
- Cloud
- Containerization
- Data Science
- Docker
- Fine Tuning
- Fintech
- GCP
- Go-to-Market
- Kubernetes
- LLM
- Machine Learning
- MCP
- Microservices
- Observability
- Proof of Concept
- Python
- PyTorch
- Quantization
- scikit-learn
- Semantic Search
- TensorFlow
- Vector Databases
- Workflow Orchestration