Cyber Senior Manager - Technology Resilience FDE

About the role

The Technical Resilience FDE Senior Manager in Deloitte Cyber is embedded in a client's environment to design, build, and ship production-grade AI capabilities using the client's own data, systems, and workflows. This role is administratively aligned to the Cyber Resilience practice.

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 directly and independently within a client's environment and codebase, including navigating unfamiliar systems and undocumented workflows
• Experience with agent orchestration or LLM application frameworks (e.g., LangChain, LlamaIndex, Model Context Protocol, Bedrock Agents, Azure AI Foundry, Vertex AI)
• Experience with GRC, ITSM, or monitoring/observability platforms (e.g., ServiceNow, Archer, Splunk, Datadog) at an architecture or platform-ownership level
• Familiarity with resilience-related frameworks or standards (e.g., NIST CSF, ISO 22301, SOC 2) useful for translating client requirements into engineering priorities - not an audit or compliance credential
• Experience designing AI-enabled use cases within resilience or continuity workflows (e.g., disaster recovery orchestration, control and evidence automation, third-party resilience monitoring) across multiple clients or engagements is a plus, though not a prerequisite
• Track record presenting technical roadmaps or audit-readiness outcomes to CISO, GRC, or other executive stakeholders
• Prior experience in a forward-deployed, embedded, or client-site engineering model (vs. offshore/remote delivery only)
• Industry depth in a regulated vertical (financial services, healthcare, public sector) and exposure to associated compliance regimes (SOX, PCI DSS, FFIEC, HIPAA, GDPR)
• Kubernetes, GitOps, and advanced cloud-native delivery patterns
• Relevant certifications - cloud (AWS/Azure/GCP) or AI/ML-specific certifications