1 day ago

Engineering Chapter Lead – AI & Agentic Platforms

ING Nederland

Amsterdam

1 day ago

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ING is seeking an Engineering Chapter Lead for AI & Agentic Platforms to lead an engineering chapter in Amsterdam. The role involves architecting enterprise-grade AI systems, leading multi-agent platform development, and driving AI strategy within the Global Workplace Services tribe. Requires 10+ years of software engineering experience with deep expertise in GenAI, LLMs, and production AI engineering.

AIOn-siteFull-timeSeniorPythonLLMs

Engineering Chapter Lead – AI & Agentic Platforms

Apply

AIOn-siteFull-timeSeniorPython

Salary

Not specified

Work Location

Amsterdam, North Holland, Netherlands, NL

Work Model

On-site

Experience Required

10 years

Employment Type

Full-time

Experience Level

Senior Engineering Leader: 10+ years in software engineering, proven leadership of engineering teams, experience leading mission-critical or large-scale engineering delivery (GJA 17–18 scope)

Core Qualifications

Technical (Must-have)

PythonLLMsGenAILangChainLangGraphLlamaIndexCopilot StudioPrompt EngineeringRAGVector DatabasesCI/CDAPIsDistributed SystemsEvent-Driven ArchitectureSRE

Soft Skills

LeadershipCoachingStakeholder InfluenceAccountabilityAmbiguity ManagementTeam Building

Key Responsibilities

•Accountable for end-to-end delivery of AI-driven capabilities through your engineering team
•Translate product priorities into clear engineering approach, architecture, and delivery plans
•Lead, coach, and develop engineers (performance, growth, hiring)
•Ensure quality, reliability, and scalability of solutions in production
•Manage delivery performance, resolve blockers, and intervene when off-track
•Architect enterprise-grade AI and multi-agent systems (Copilot, GenAI agents, orchestration frameworks)
•Lead design of agentic workflows (multi-agent reasoning, routing, integrations)
•Define and implement AI platform architecture, RAG pipelines, and integrations with enterprise systems
•Ensure AI systems are scalable, secure, observable, and compliant
•Drive technical decision-making, architecture reviews, and engineering standards
•Build and scale Copilot / M365 agents and enterprise AI use-cases
•Design API-driven integrations with systems (ServiceNow, Azure DevOps, M365, Graph, Workday, etc)
•Implement AI-enabled workflows embedded in enterprise platforms and SDLC processes
•Define measurement frameworks, evaluation metrics, and feedback loops for AI systems
•Ensure AI reliability, monitoring, and continuous improvement loops
•Drive AI safety, governance, observability, and risk mitigation frameworks
•Guarantee compliance with security, data protection, and regulatory standards
•Contribute to AI strategy, roadmap, and long-term platform evolution
•Lead innovation and experimentation in AI-first workplace solutions
•Translate emerging AI trends into practical ING use cases and capabilities

AIAgentic PlatformsEngineering Chapter LeadBankingGenAILLMsCopilotPythonEnterprise IntegrationSenior

Key Responsibilities

•Accountable for end-to-end delivery of AI-driven capabilities through your engineering team

•Translate product priorities into clear engineering approach, architecture, and delivery plans

•Lead, coach, and develop engineers (performance, growth, hiring)

•Ensure quality, reliability, and scalability of solutions in production

•Manage delivery performance, resolve blockers, and intervene when off-track

•Architect enterprise-grade AI and multi-agent systems (Copilot, GenAI agents, orchestration frameworks)

•Lead design of agentic workflows (multi-agent reasoning, routing, integrations)

•Define and implement AI platform architecture, RAG pipelines, and integrations with enterprise systems

•Ensure AI systems are scalable, secure, observable, and compliant

•Drive technical decision-making, architecture reviews, and engineering standards

•Build and scale Copilot / M365 agents and enterprise AI use-cases

•Design API-driven integrations with systems (ServiceNow, Azure DevOps, M365, Graph, Workday, etc)

•Implement AI-enabled workflows embedded in enterprise platforms and SDLC processes

•Define measurement frameworks, evaluation metrics, and feedback loops for AI systems

•Ensure AI reliability, monitoring, and continuous improvement loops

•Drive AI safety, governance, observability, and risk mitigation frameworks

•Guarantee compliance with security, data protection, and regulatory standards

•Contribute to AI strategy, roadmap, and long-term platform evolution

•Lead innovation and experimentation in AI-first workplace solutions

•Translate emerging AI trends into practical ING use cases and capabilities