Generative AI Engineer

You will work directly with clients to design, build, and deliver production-grade AI systems, embedding into complex environments to solve real operational and data challenges.

This role blends software engineering, AI system design, and client delivery, with a strong focus on scalable, production-ready solutions.

What you'll be doing

• Build and evolve AI-driven origination and workflow platforms, including deal intake, pipeline management, relationship intelligence, approvals, and execution processes
• Design and implement microservices and APIs that integrate data across enterprise systems such as CRMs, internal platforms, and external data providers
• Translate complex financial and operational logic - including pipeline structures, attribution models, allocations, and reporting frameworks - into scalable technical systems
• Architect and optimise data models and database layers, including schema design, indexing strategies, query tuning, and stored procedures
• Develop and maintain CI/CD pipelines and engineering tooling to support reliable, frequent, and high-quality deployments
• Implement event-driven architectures using Kafka, enabling real-time data processing, system decoupling, and auditability
• Ensure high performance, reliability, and scalability across distributed systems, including observability, monitoring, and production readiness
• Partner with product, operations, and investment stakeholders to refine requirements and deliver iterative solutions in Agile environments

• 8+ years of hands-on software engineering experience across Python and object-oriented languages (Java or C++)
• Strong background delivering production-grade full-stack or backend systems
• Deep understanding of cloud architecture (AWS, Azure, or GCP) with a focus on scalable and secure systems
• Strong proficiency in Python and SQL, plus experience with either Java or .NET
• Solid DevOps experience including Git, CI/CD pipelines, and containerisation (Docker, Jenkins or equivalent tools)
• Proven experience designing and building microservices-based architectures in distributed systems
• Advanced knowledge of database systems, including schema design, performance tuning, and query optimisation
• Experience working with event-driven architectures and messaging systems such as Kafka
• Familiarity with both NoSQL and NewSQL databases, including trade-offs and use cases
• Ability to take ambiguous business requirements and turn them into clear technical designs and working systems

AI Engineering capability (essential)

All engineers in this team are expected to meaningfully apply AI in production systems, including:

• Building and deploying LLM-based applications in production environments
• Designing agentic workflows and multi-step AI systems
• Implementing RAG (Retrieval-Augmented Generation) pipelines
• Working with OpenAI or Anthropic APIs
• Using vector databases and embedding-based search systems
• Applying prompt engineering techniques effectively
• Building AI evaluation, monitoring, and observability frameworks
• Understanding ML fundamentals where relevant (embeddings, fine-tuning, context engineering)