Vista previa de la oferta
Vice President - AI/ML Technical Developer
lead · Technology / Software Development
You are a strategic thinker passionate about driving solutions in AI/ML. You have found the right team.
As a Senior AI/ML Technical Developer within the finance agentic platform product team, you will act as the senior technical enabler for decision science and product partners, bringing advanced AI capabilities into production. You will translate prototypes, analytical approaches, and product requirements into repeatable platform integrations and production-ready implementations. You will own the end-to-end onboarding lifecycle for AI/ML and LLM use cases, from intake and requirements clarification through reference architecture, integration patterns, evaluation criteria, security alignment, deployment, and run-and-operate readiness in a regulated environment. You will define and promote the agentic framework roadmap, shaping how the platform evolves to support a growing portfolio of finance use cases. You will standardise patterns for RAG and agents, evaluation harnesses, guardrails, observability, and integration contracts, ensuring they align with product strategy and platform adoption goals. You will build and maintain high-quality platform components in Python, applying modern engineering practices such as automated testing, robust design patterns, structured code reviews, and disciplined version control. You will apply LLM techniques—including prompt engineering, retrieval-augmented generation, fine-tuning, and agentic frameworks—to create reusable patterns that connect model and agent behaviour to measurable business outcomes. You will partner closely with technology delivery teams to deliver roadmap items on time, managing dependencies, risks, and release readiness to meet stakeholder expectations. You will ensure operational stability, monitoring, and resilience of ML and agentic systems in production through comprehensive observability, quality regression testing, disciplined releases, and effective incident response. You will communicate complex technical topics clearly to senior stakeholders and ensure all technical decisions align with governance, risk, and control requirements for responsible AI.
Job responsibilities
You will support the end-to-end onboarding of AI/ML and LLM use cases from decision science and product teams onto the finance agentic platform. This includes establishing a clear intake and onboarding process, translating requirements into repeatable integration patterns, and ensuring each use case meets production readiness expectations for reliability, maintainability, and regulated operations.
You will define, prioritise, and drive the agentic framework roadmap in alignment with product strategy and platform adoption goals. You will identify capability gaps, translate them into well-scoped epics and stories with clear acceptance criteria, and ensure the roadmap delivers standards and reusable components that materially accelerate onboarding and reuse.
You will partner closely with technology delivery teams to deliver prioritised roadmap items on time. You will support joint planning and sequencing, manage cross-team dependencies, surface risks and trade-offs early, escalate issues appropriately, and coordinate release readiness so deliveries are predictable and aligned to stakeholder expectations.
You will build and maintain high-quality platform components in Python, applying modern engineering practices including automated testing, thoughtful design patterns, structured code reviews, and disciplined version control. You will contribute to architecture decisions for platform services, SDKs, templates, and integration scaffolding, making pragmatic trade-offs across reliability, latency, cost, and long-term maintainability.
You will apply LLM techniques, including prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and agentic frameworks and skills patterns, in ways that standardise how finance use cases are implemented on the platform. You will define evaluation methods and success criteria that connect model and agent behaviour to business outcomes and measurable quality metrics.
You will use AI coding tools (for example, Claude Code and GitHub Copilot) as a productive part of day-to-day development, while maintaining strong judgment on validation, secure coding, confidentiality, licensing considerations, and when human-led engineering and deeper review are required.
You will ensure the operational stability, monitoring, and resilience of ML and agentic systems running in production on the platform. This includes implementing monitoring and tracing, alerting, quality regression testing for model and agent changes, disciplined release practices, and incident response and root-cause analysis that drive durable fixes and improve onboarding reliability.
You will communicate complex technical topics clearly and with confidence to senior business and technology stakeholders. You will help define success metrics and articulate clear objectives and key results (OKRs) aligned to platform outcomes such as onboarding cycle time, reuse, reliability, and model or agent quality, enabling transparent tracking of progress.
You will apply strong judgment to align all technical decisions with governance, risk, and control requirements for responsible AI, including data handling, model risk considerations, auditability, platform guardrails, and controlled releases with appropriate human oversight scaled to the criticality of each use case.
Required qualifications, capabilities, and skills
Candidates must have 5+ years of professional experience building and delivering AI/ML solutions in production environments, with a track record of end-to-end ownership from design through to operational stability.
You bring applied experience working with agentic platforms or agentic frameworks and understand the architectural and operational considerations that distinguish agentic AI systems from conventional ML pipelines.
You have demonstrable experience collaborating across cross-functional teams, including product owners, data scientists or decision science teams, and technology delivery partners, and can operate effectively as a product-embedded technical anchor bridging these groups.
You have advanced proficiency in Python and can write and review production-quality code with a focus on reliability, maintainability, and performance. You bring strong software engineering fundamentals including system design, testing discipline, code review practices, and operational ownership.
You have hands-on experience building, evaluating, and deploying machine learning models and LLM solutions into production, including designing evaluation approaches that meaningfully measure model quality and business impact.
You have experience delivering within a financial services environment or a similarly regulated industry, with an understanding of the governance, risk, and control expectations that apply to AI and data systems in such settings.
You can work across multiple technical disciplines to solve complex problems pragmatically, and you communicate with clarity and confidence, including the ability to explain technical trade-offs and delivery risks to non-technical stakeholders.
Preferred qualifications, capabilities, and skills
Formal training or certification in software engineering concepts, or demonstrably equivalent applied expertise developed through professional practice, is preferred.
Practical depth in LLM techniques is strongly preferred, including prompt engineering, RAG, fine-tuning, and experience designing and implementing agentic patterns responsibly and at production scale.
Fluency with AI coding tools (for example, Claude Code and GitHub Copilot) as a core part of day-to-day development is preferred, alongside demonstrated judgment on output validation, security, and appropriate usage boundaries.
Experience contributing to or owning a technical roadmap in a product engineering context is preferred, including identifying capability gaps, defining requirements, and aligning delivery priorities with product and technology stakeholders.
Demonstrated strength in defining success metrics and translating product and business intent into clear technical objectives, measurable results, and well-formed OKRs is preferred.
Experience operating in environments with strict governance and responsible AI expectations is preferred, including the ability to design solutions that support auditability, controlled releases, model risk documentation, and appropriate human oversight.