AI Architect

HCL Technologies B.V. Netherland; Amsterdam; On-site; EUR 120,000 - 180,000; Full time

Job summary

HCL Technologies B.V. is seeking a hands-on AI leader to design and implement enterprise-grade AI solutions within Wholesale Banking and Financial Markets.

The role requires translating complex regulatory and business requirements into scalable AI architectures and delivery roadmaps. You will guide engineering teams through all phases of design, development, integration, testing, deployment, and production support, leveraging tools like GitHub Copilot, Google ADK, GCP, Vertex AI, and Gemini.

Qualifications

• Hands-on experience designing and implementing enterprise-grade AI solutions.
• Ability to translate business and regulatory requirements into scalable AI architectures.
• Strong understanding of Financial Markets products and processes.

Responsibilities

• Lead solution design, development, integration, testing, deployment and production support for AI initiatives.
• Guide engineering teams across end-to-end delivery in Wholesale Banking contexts.
• Identify opportunities for AI, Generative AI, and agentic automation across front-, middle-, and back-office functions.

Skills

AI architecture

Team leadership

Stakeholder communication

Regulatory compliance understanding

Tools

GitHub Copilot

Google ADK

Google Cloud Platform

Vertex AI

Gemini

Job description

• The candidate should have strong hands-on experience in designing and implementing enterprise-grade AI solutions, preferably within Wholesale Banking and Financial Markets environments.
• Hands-on expertise with GitHub Copilot, Google Agent Development Kit (ADK), Google Cloud Platform (GCP), Vertex AI, Gemini, and related Google AI technologies is essential.
• The role requires the ability to translate complex business and regulatory requirements into scalable AI architectures, implementation strategies, and delivery roadmaps.
• The candidate should possess a strong understanding of Financial Markets products and processes, including areas such as Foreign Exchange, Fixed Income, Rates, Credit, Equities, Commodities, and Derivatives.
• Experience across the Financial Markets trade lifecycle—including pricing, trade capture, confirmations, settlements, collateral management, risk management, regulatory reporting, and post-trade operations—will be highly preferred.
• The candidate should understand front-office, middle-office, and back-office functions within Wholesale Banking and be able to identify opportunities for applying AI, Generative AI, and agentic automation across these functions.
• Familiarity with Financial Markets data, including market data, reference data, trade data, pricing data, risk data, and unstructured research or client information, is desirable.
• The role requires awareness of key banking considerations such as model risk management, explainability, data privacy, information security, auditability, regulatory compliance, and responsible AI.
• Experience in developing AI solutions for use cases such as trader and sales enablement, market intelligence, research summarisation, client insights, trade surveillance, risk analytics, regulatory compliance, operations automation, and exception management would be advantageous.
• The candidate should be capable of guiding engineering teams across solution design, development, integration, testing, deployment, and production-support phases.
• Experience in building agentic AI solutions, Generative AI applications, Retrieval-Augmented Generation solutions, and cloud-native AI platforms is highly preferred.
• The candidate should be able to integrate AI solutions with enterprise banking platforms, Financial Markets systems, APIs, data platforms, and workflow or orchestration tools.
• The person should be comfortable working closely with client stakeholders, Financial Markets business teams, product owners, quantitative teams, risk and compliance functions, data teams, and technology teams.
• Strong problem-solving skills and the ability to make pragmatic architectural and engineering decisions in a fast-paced, regulated delivery environment are required.
• The candidate should demonstrate strong stakeholder-management and communication skills, with the ability to explain complex AI concepts to both technical and non-technical audiences.
• Prior experience in Wholesale Banking, Corporate and Investment Banking, Capital Markets, Treasury, or Financial Services is strongly preferred.
• Overall, the client requires a hands-on AI leader who can combine AI architecture, engineering depth, Financial Markets domain knowledge, and end-to-end delivery ownership.

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