Cyber Senior Manager - Technology Resilience FDE

About the role

This role is administratively aligned to the Cyber Resilience practice and involves embedding in a client's environment to design, build, and ship production-grade AI capabilities using the client's own data, systems, and workflows. The role combines strong personal engineering depth with the ability to lead and develop other engineers, own the technical roadmap, and build reusable accelerators.

Responsibilities

• Designing and hands-on building AI-enabled solutions (agents, retrieval/RAG pipelines, automation workflows) directly inside a client's environment, using their live data and systems
• Setting and owning standards for AI production practices - evaluation, guardrails, observability, reliability, security, and cost/performance management - across multiple solutions or engagements
• Leading, mentoring, and managing the performance and career development of one or more Engineering Managers and their teams across one or more client engagements
• Architecting the AI capability roadmap across multiple workstreams or operational domains for a client or portfolio of clients - applied, for example, to disaster recovery orchestration, control and evidence automation, and third-party resilience monitoring
• Engaging client stakeholders (e.g., CISO, resilience and GRC leadership) to prioritize automation of controls, monitoring, and evidence workflows that support their audit and compliance needs
• Translating client business needs - including resilience use cases such as continuity planning and recovery orchestration - into working, production-grade AI technical solutions
• Leading the hands-on design, integration, deployment, and operation of production-grade solutions, including troubleshooting and resolving technical issues within scope
• Owning technical solutioning during pursuits, including demonstrations, proofs of concept, prototypes, effort estimation, and pricing inputs across multiple opportunities
• Creating new reusable accelerators and scaling their adoption across teams and engagements, backed by documentation and knowledge transfer
• Managing client delivery by overseeing scope, timelines, quality, customer satisfaction, and continuous improvement across engagements
• Owning the technical roadmap across engagements and contributing to broader practice capability development, including hiring, training curricula, and reusable IP

Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field; alternatively, equivalent demonstrated experience
• 12-15+ years of hands-on software engineering experience building and deploying production-grade systems using one or more of the following - Python, Java, or Node.js
• 7+ years of experience translating client or business requirements into target-state solution architectures using REST APIs, microservices, event-driven architectures, or serverless components
• 3+ years of experience delivering solutions on Amazon Web Services, Microsoft Azure, or Google Cloud Platform, including containers, continuous integration and continuous delivery pipelines, and version control tools
• 3+ years of hands-on experience designing, building, and deploying generative AI or large language model solutions (e.g., agents, RAG, tool-calling) in a client or production environment - beyond proof-of-concept
• 3+ years of experience owning and setting standards for production AI engineering practices - evaluation, guardrails, observability, reliability, security, and cost/performance management - across multiple solutions or engagements
• 3+ years of experience leading and developing engineering teams, including direct management of Engineering Managers or equivalent technical leads, with accountability for performance management and career development
• Experience architecting AI-enabled solutions across multiple workstreams or operational domains, translating varied client requirements into a coherent technical roadmap
• Experience contributing to practice or team capability beyond individual engagements - for example, mentoring engineering managers, shaping hiring or training practices, or developing reusable accelerators and IP
• Experience owning client enablement at scale - workshops, demonstrations, adoption planning, and operational handoff - across multiple engagements or a portfolio of clients
• Ability to work independently and collaborate as part of a team, effective written and verbal communication skills, meticulous attention to detail and quality of work product, ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
• Proven ability to build and sustain professional relationships, lead projects or workstreams and meet deadlines
• Ability to build and oversee automation that integrates with monitoring, ITSM, or GRC platforms to support control monitoring, evidence collection, and response workflows across multiple engagements
• Ability to work directly and independently within a client's environment and codebase, including navigating unfamiliar systems and undocumented workflows
• Ability to create new reusable AI accelerators, tools, or frameworks and drive their adoption and scaling across teams and engagements
• Ability to travel 25-50%, on average, based on the work you do and the clients and industries/sectors you serve