Job description

At Deloitte, Forward Deployed Engineers (FDE) don’t just build AI solutions, they help clients turn AI ambition into enterprise-scale impact, pairing leading class engineering with pod-based delivery and vertical expertise. If you thrive at the intersection of product, engineering, problem-solving, and client impact, this role puts you at the forefront of AI transformations. Recruiting for this role ends on 10/30/2026 Work you’ll do As a Lead Microsoft AI&Data FDE, you will serve as the senior practitioner-leader embedded directly with our most strategic clients, leading forward-deployed engineering pods that develop and deploy GenAI solutions into production for Deloitte’s most strategic clients. You’ll set technical direction, remove delivery blockers, and stay hands-on; designing, reviewing, and debugging systems with the team. You’ll translate engineering trade-offs into clear decisions for client leaders when needed. Your ability to influence decisions at the C-suite level, while maintaining hands-on technical credibility, is what sets you apart. Pods under your leadership may be deployed onshore with clients or in hybrid onshore/offshore configurations, leveraging Deloitte's global delivery capability to maximize speed and scale. Client Engagement • Serve as the senior client-facing presence, building trusted advisor relationships as the senior engineering partner for client product, data, and platform leaders • Lead executive-level discovery, define success metrics (quality, latency, cost, adoption, risk) and a phased plan from prototype to production and scaling • Navigate organizational complexity and influence to align executive sponsors, IT leadership, and business owners around a shared vision • Represent Deloitte's FDE capability in client pursuits, executive briefings, and platform partner engagements—contributing to pipeline development and deal shaping. Cross-Functional Pod Leadership & Program Governance • Lead FDE pods of 2–5 onshore anchored and offshore supported engineers, owning execution, resource management, escalations and overall delivery health • Enforce delivery standards across the pod: sprint cadences, stakeholder communication plans, risk management, and quality gates • Coordinate multi-pod or multi-workstream engagements, ensuring reliable architecture and consistent client experience. • Mentor and develop junior FDEs GenAI Solution Development • Architect and oversee delivery of LLM-enabled applications including copilots, agentic workflows, assistants, and knowledge search experiences using one or more enterprise AI platforms (see Platform Requirements below) • Set direction for prompt engineering, tool-use patterns, and human-in-the-loop controls • Govern end-to-end RAG pipeline design—including ingestion, chunking, embedding, vector retrieval, and hybrid search—ensuring production-grade quality and scalability. • Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance; ensure the pod meets agreed engineering quality bars to these standards. Engineering & Data Foundations • Review and contribute to production-quality code • Guide architecture of data pipelines powering GenAI use cases • Enforce strong data management, testing, CI/CD, logging, versioning, and documentation practices • Deep familiarity with cloud environments (AWS, Azure, and/or Google Cloud) The team AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements. Required qualifications • Bachelor's degree (or equivalent) in Computer Science, Data Science or Engineering. • 7+ years of experience in software engineering, data engineering, data science, or analytics engineering. • 1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments • 1+ years of experience with Microsoft AI&Data including hands on experience with Azure AI Foundry • 1+ years of experience leading project workstreams/engagements and translating business problems into AI solutions • 1+ years of experience building reliable, maintainable, and well-documented code • Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve • Limited immigration sponsorship may be available Preferred qualifications • Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking) • Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments • Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation • Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management • Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures • Experience operating within hybrid onshore/offshore teams • Familiarity with security, privacy, and compliance considerations The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $189,200 to $372,900. You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Requirements

Must-haves

Recruiting for this role ends on 10/30/2026
Work you’ll do
Client Engagement
Serve as the senior client-facing presence, building trusted advisor relationships as the senior engineering partner for client product, data, and platform leaders
Lead executive-level discovery, define success metrics (quality, latency, cost, adoption, risk) and a phased plan from prototype to production and scaling
Navigate organizational complexity and influence to align executive sponsors, IT leadership, and business owners around a shared vision
Represent Deloitte's FDE capability in client pursuits, executive briefings, and platform partner engagements—contributing to pipeline development and deal shaping.
Cross-Functional Pod Leadership & Program Governance
Lead FDE pods of 2–5 onshore anchored and offshore supported engineers, owning execution, resource management, escalations and overall delivery health
Enforce delivery standards across the pod: sprint cadences, stakeholder communication plans, risk management, and quality gates
Coordinate multi-pod or multi-workstream engagements, ensuring reliable architecture and consistent client experience.
Mentor and develop junior FDEs
GenAI Solution Development
Architect and oversee delivery of LLM-enabled applications including copilots, agentic workflows, assistants, and knowledge search experiences using one or more enterprise AI platforms (see Platform Requirements below)
Set direction for prompt engineering, tool-use patterns, and human-in-the-loop controls
Govern end-to-end RAG pipeline design—including ingestion, chunking, embedding, vector retrieval, and hybrid search—ensuring production-grade quality and scalability.
Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance; ensure the pod meets agreed engineering quality bars to these standards.

Nice-to-haves

Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)
Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments
Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation
Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management
Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures
Experience operating within hybrid onshore/offshore teams
Familiarity with security, privacy, and compliance considerations

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aillmci/cdawsazureml

Platform: avature
Effort: medium

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