Cyber Senior Manager - Technology Resilience FDE

Deloitte•Jersey City, NJ

75d•$189,200 - $372,900•Onsite

About The Position

As a Senior 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, while also leading the broader team and technical roadmap delivering that work. This role is administratively aligned to the Cyber Resilience practice, and the applied use cases you and your team 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 engineering and leadership work supports, not prerequisites requiring deep resilience or audit domain credentials. You will combine strong personal engineering depth with the ability to lead and develop other engineers, own the technical roadmap across multiple workstreams, 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
Proven ability to mentor, develop, and manage the performance of other engineers and engineering managers
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
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
Experience creating new reusable AI accelerators, tools, or frameworks and driving their adoption and scaling across teams and engagements
Ability to work directly and independently within a client's environment and codebase, including navigating unfamiliar systems and undocumented workflows
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 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) 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
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
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
Owning client enablement across engagements — workshops, demonstrations, adoption planning, operational handoff, and training curricula — so client teams can independently operate and extend delivered AI capabilities
Managing client delivery by overseeing scope, timelines, quality, customer satisfaction, and continuous improvement across engagements
Creating new reusable accelerators and scaling their adoption across teams and engagements, backed by documentation and knowledge transfer
Owning the technical roadmap across engagements and contributing to broader practice capability development, including hiring, training curricula, and reusable IP