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.
Work you'll do
As a Senior Frontier GenAI FDE, you will work side by side with senior functional and technical client team members to rapidly prototype and deliver high-impact GenAI-enabled solutions. This requires a highly motivated practitioner who moves with speed and precision, building working software, engaging confidently with senior stakeholders and engineers to bring measurable business impact from day one. Additional responsibilities include:
Client Engagement
- Embed with clients to identify business needs and translate high-value GenAI use cases into solutions.
- Partner with leaders, product owners, architects, and engineers to align priorities and delivery.
- Lead working sessions to shape solutions and drive client outcomes.
- Prototype and deliver working AI solutions using industry expertise and emerging capabilities.
- Contribute independently within an FDE pod while mentoring newer team members.
- Coach client teams and end users on platform capabilities and AI enablement, while building trusted relationships, managing expectations, and supporting long-term engagement success.
- Drive end-to-end sales and delivery support by developing demos/POCs, contributing to proposals and orals, articulating business value, and documenting solutions for smooth client handoff and knowledge transfer.
- Strengthen team and organizational impact by mentoring other FDEs through design/code reviews and feedback, while contributing reusable components to intellectual capital.
Solution Engineering
- Build AI-enabled solutions, agentic platforms, and workflows across enterprise AI platforms.
- Develop scalable AI engineering patterns, tool-use approaches, and human-in-the-loop controls.
- Apply architecture decisions that balance quality, safety, latency, cost, and model risk.
- Deliver production-quality code using strong practices in testing, CI/CD, logging, versioning, and documentation.
- Design extensible functionality, support sprint sizing, and align solutions with senior team members.
- Contribute reusable assets including code, prompt libraries, runbooks, and reference implementations.
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.
- 5+ 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 one of the following Frontier GenAI Platforms: Anthropic, Google or Open AI, including hands on experience with one of the following key platforms/products; Claude API, Claude for Enterprise, tool use, extended thinking, Claude Code, Gemini API, Vertex AI Agent Builder, Grounding, Google Workspace integration, GPT-4o, Assistants API, Responses API, OpenAI Agents SDK
- 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 considerat