Senior Forward Deployed Engineer, Microsoft AI&Data

About the role

As a Senior Microsoft AI&Data Forward Deployed Engineer (FDE) at Deloitte, you'll work side by side with senior functional and technical client team members to rapidly prototype and deliver high-impact GenAI-enabled solutions. This role requires a highly motivated practitioner who moves with speed and precision, building working software and engaging confidently with senior stakeholders to drive measurable business impact from day one.

Responsibilities

• 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, building trusted relationships and managing expectations.
  • Drive end-to-end sales and delivery support by developing demos/POCs, contributing to proposals, articulating business value, and documenting solutions for smooth handoff.
  • Strengthen team impact by mentoring other FDEs through design/code reviews and 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.

Requirements

• 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 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 in 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.

Pay

The wage range for this role is $155,600 to $306,800. This range takes into account factors such as skill sets, experience, training, licensure, certifications, and other business and organizational needs. You may also be eligible to participate in a discretionary annual incentive program, subject to program rules, where awards depend on individual and organizational performance.