Check out this job at Deloitte, Senior Forward Deployed Engineer, Microsoft AI&Data at Deloitte6

Compensation

$155,600 – $306,800/yr

Location: Atlanta, Georgia · Austin, Texas · +44
Type: Full-time
Level: senior
Posted: May 21, 2026

Description

Deloitte is hiring a Senior Forward Deployed Engineer, Microsoft AI&Data to partner with senior client and technical teams on rapid prototyping and delivery of enterprise-scale GenAI solutions. The role covers client engagement, solution architecture, production-quality software, AI platforms and workflows, demos and POCs, sales and delivery support, and mentoring. It requires a bachelor's degree in a relevant field, at least five years of software, data, data science, or analytics engineering experience, and experience with GenAI/LLM solutions, Microsoft AI&Data, Azure AI Foundry, and project workstreams. The role requires approximately 50% travel and offers a disclosed base salary range of $155,600 to $306,800.

Required Skills

GenAI
Azure AI Foundry
Spark
Airflow
dbt
MLOps
LLMOps
CI/CD
APIs

Additional Information

Minimum Education: Bachelor's degree (or equivalent) in Computer Science, Data Science, or Engineering
Required Previous Experiences: 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 or engagements and translating business problems into AI solutions
1+ years of experience building reliable, maintainable, and well-documented code
Preferred Qualifications: Experience with AWS, Azure, or Google Cloud and common platform services
Ability to work directly with 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 feature engineering, experimentation, or model evaluation
Experience with MLOps or LLMOps practices, including evaluation frameworks, model monitoring, and prompt management
Experience integrating LLM solutions with enterprise systems through APIs, microservices, or event-driven architectures
Experience operating within hybrid onshore/offshore teams
Familiarity with security, privacy, and compliance considerations