AI Solutions Architect / Engineering Lead

Role purpose Lead the architecture and engineering delivery of AI solutions across the brokerage. Design secure, maintainable systems that connect AI models, data services and enterprise applications, and guide engineers from prototype to reliable production operation. Balance solution quality, delivery speed, integration complexity and cost within the company’s technology standards. Key responsibilities Architecture and technical direction • Translate approved product requirements into end-to-end designs covering application services, retrieval, model access, data flows, system integration, identity, observability and deployment. • Define reusable patterns for retrieval-augmented generation, agent workflows, model-serving interfaces and event-driven automation, avoiding unnecessary complexity. • Evaluate build-versus-buy options, model providers and deployment approaches against performance, security, data handling, integration effort, resilience and total cost. • Produce architecture diagrams, design decisions, interface contracts and non-functional requirements, including latency, throughput, availability and recovery expectations. Engineering leadership and implementation • Lead technical discovery, delivery planning, code reviews and implementation for AI services integrated with CRM, client portals, support tools, payments systems and trading back offices. • Remain hands-on with critical components, reference implementations and complex debugging; mentor engineers and establish maintainable coding and testing practices. • Design agent permissions, tool boundaries, approval steps and transaction safeguards so probabilistic model outputs cannot directly trigger unrestricted sensitive actions. • Work with Data Engineering on trusted datasets and retrieval sources, and with MLOps on reproducible deployment, versioning, monitoring and rollback. Reliability and technical governance • Define secure handling of client and trading information, including access enforcement, secrets management, data minimisation and separation of development and production environments. • Partner with QA and Evaluation to establish release criteria, failure scenarios and traceability across models, prompts, retrieved content and application versions. • Lead performance optimisation, incident diagnosis and remediation; implement bounded retries, timeouts, provider fallbacks and safe degradation where appropriate. • Maintain technical roadmaps and debt registers, review vendor dependencies and communicate architecture risks and resource requirements to the CTO and Head of AI. Essential experience and qualifications • 8+ years in software engineering, including 3+ years in solution architecture or technical leadership and delivery of production AI or ML-enabled systems. • Strong hands-on Python and backend engineering skills, with experience designing APIs, distributed services, asynchronous processing and reliable integrations. • Practical experience delivering LLM applications, retrieval systems, agent tool use and evaluation frameworks within production software. • Experience with cloud infrastructure, containers, CI/CD, observability, database design and secure enterprise identity and access management. • Ability to assess model quality, response time, cost and security trade-offs and turn prototypes into supportable services. • Experience leading engineers and technical decisions in financial services or another environment with sensitive data and strong control requirements. • Bachelor’s degree in Computer Science, Software Engineering or a related discipline, or equivalent substantial engineering experience. Preferred experience • Direct experience with FX/CFD platforms, MT4/MT5 integrations, liquidity connectivity or brokerage back-office architecture. • Experience with vector search, event streaming, private model hosting and multi-provider AI infrastructure. • Experience scaling engineering teams and developing shared internal platforms. Show more Show less

8+ years in software engineering, including 3+ years in solution architecture or technical leadership and delivery of production AI or ML-enabled systems. Strong hands-on Python and backend engineering skills, with experience designing APIs, distributed services, asynchronous processing and reliable integrations. Practical experience delivering LLM applications, retrieval systems, agent tool use and evaluation frameworks within production software. Experience with cloud infrastructure, containers, CI/CD, observability, database design and secure enterprise identity and access management. Ability to assess model quality, response time, cost and security trade-offs and turn prototypes into supportable services. Experience leading engineers and technical decisions in financial services or another environment with sensitive data and strong control requirements. Bachelor’s degree in Computer Science, Software Engineering or a related discipline, or equivalent substantial engineering experience.

Architecture and technical direction: Translate approved product requirements into end-to-end designs covering application services, retrieval, model access, data flows, system integration, identity, observability and deployment. Define reusable patterns for retrieval-augmented generation, agent workflows, model-serving interfaces and event-driven automation, avoiding unnecessary complexity. Evaluate build-versus-buy options, model providers and deployment approaches against performance, security, data handling, integration effort, resilience and total cost. Produce architecture diagrams, design decisions, interface contracts and non-functional requirements, including latency, throughput, availability and recovery expectations. Engineering leadership and implementation: Lead technical discovery, delivery planning, code reviews and implementation for AI services integrated with CRM, client portals, support tools, payments systems and trading back offices. Remain hands-on with critical components, reference implementations and complex debugging; mentor engineers and establish maintainable coding and testing practices. Design agent permissions, tool boundaries, approval steps and transaction safeguards so probabilistic model outputs cannot directly trigger unrestricted sensitive actions. Work with Data Engineering on trusted datasets and retrieval sources, and with MLOps on reproducible deployment, versioning, monitoring and rollback. Reliability and technical governance: Define secure handling of client and trading information, including access enforcement, secrets management, data minimisation and separation of development and production environments. Partner with QA and Evaluation to establish release criteria, failure scenarios and traceability across models, prompts, retrieved content and application versions. Lead performance optimisation, incident diagnosis and remediation; implement bounded retries, timeouts, provider fallbacks and safe degradation where appropriate. Maintain technical roadmaps and debt registers, review vendor dependencies and communicate architecture risks and resource requirements to the CTO and Head of AI.