Cyber Manager - Technology Resilience FDE

Deloitte•Jersey City, NJ

75d•$155,600 - $306,800•Onsite

About The Position

As 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, and the applied use cases you build will typically span disaster recovery orchestration, control and evidence collection, continuity and recovery planning, and third-party resilience monitoring — but these are application areas your AI engineering work supports, not prerequisites requiring deep resilience or audit domain credentials. You will combine strong engineering depth with the judgment to translate ambiguous client problems into working AI systems, shape technical solutions during pursuits, and build reusable accelerators that raise the bar across engagements. Recruiting for this role ends on 12/31/2026.

Requirements

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
Ability to build and sustain professional relationships, lead projects or workstreams and meet deadlines
Ability to mentor and provide clear guidance to others
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
Ability to build automation that integrates with monitoring, ITSM, or GRC platforms to support control monitoring, evidence collection, and response 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
Limited immigration sponsorship may be available

Nice To Haves

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

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