Cyber Manager - Technology Resilience

About the role

A Manager, AI Engineering (Forward Deployed Engineer) in Deloitte Cyber, you will be 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
• Ensuring deployed AI systems meet production bars for evaluation, guardrails, observability, reliability, security, and cost/performance management
• Building automated controls and response workflows that support disaster recovery orchestration and continuity and recovery operations
• Building AI-enabled control and evidence collection capabilities - automating inventory, monitoring, and evidence gathering to produce audit-ready evidence across cybersecurity, continuity, and third-party resilience programs
• Translating client business needs - including resilience use cases such as continuity planning and recovery orchestration - into working, production-grade AI technical solutions aligned to target architecture
• Leading the hands-on design, integration, deployment, and operation of production-grade solutions, including troubleshooting and resolving technical issues within scope
• Shaping technical solutions during pursuits by leading demonstrations, proofs of concept, prototypes, effort estimation, and pricing inputs
• Leading client-facing workshops, demonstrations, and training sessions to drive adoption of new AI capabilities, and supporting operational handoff so client teams can run and maintain what you build
• Managing client delivery by overseeing scope, timelines, quality, customer satisfaction, and continuous improvement across engagements
• Contributing to and extending existing reusable accelerators, documentation, and engineering best practices to build team and client capability
• Mentoring engineers and leading individual workstreams within the engagement

Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field; alternatively, equivalent demonstrated experience
• 8-10+ 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
• 5+ years of experience translating client or business requirements into target-state solution architectures using REST APIs, microservices, event-driven architectures, or serverless components
• 2+ 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
• 2+ 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
• Hands-on experience applying production AI engineering practices - evaluation, guardrails, observability, reliability, security, and cost/performance management - to deployed models and agentic systems
• Ability to work directly and independently within a client's environment and codebase, including navigating unfamiliar systems and undocumented workflows
• Experience leading client enablement activities - workshops, demonstrations, adoption planning, and operational handoff - to help client teams adopt and sustain delivered solutions
• Experience contributing to and extending reusable AI accelerators, tools, or frameworks that speed up delivery across engagements
• Exposure to disaster recovery, business continuity, or third-party resilience concepts is a plus but not required - domain onboarding will be provided
• Ability to travel 25-50%, on average, based on the work you do and the clients and industries/sectors you serve

Preferred

• Front-end / full-stack breadth - JavaScript/TypeScript and a modern UI framework (React / Next.js) for building demo apps and lightweight delivery tooling leveraging agentic coding tools (e.g., Claude Code, Codex, Cursor, etc.)
• 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) relevant to control monitoring and evidence automation
• Familiarity with control frameworks or standards (NIST CSF, ISO 22301, SOC 2) sufficient to model them in code - audit or assessor experience not required
• Experience designing AI-enabled use cases within resilience or continuity workflows (e.g., disaster recovery orchestration, control and evidence automation, third-party resilience monitoring) is a plus, though not a prerequisite
• 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
• Familiarity with ML frameworks (PyTorch, TensorFlow) and model evaluation
• Relevant certifications - cloud (AWS/Azure/GCP) or AI/ML-specific certifications