Forward Deployment Engineer (FDE) – AI Platform & Agentic Transformation

Forward Deployment Engineer (FDE) – AI Platform & Agentic Transformation

Location :: USA

Experience

8–15 Years

Role Summary

We are seeking a highly motivated Forward Deployment Engineer (FDE) to bridge the gap between enterprise customers and product engineering teams. The FDE will work directly with business stakeholders, architects, and engineering teams to deploy, customize, and operationalize AI-powered solutions, Azure AI platforms, and agentic workflows in complex enterprise environments.

This role combines elements of Solution Architecture, Platform Engineering, Consulting, Customer Success, and Software Engineering . The ideal candidate is comfortable moving between executive discussions, solution design, hands-on development, and production deployment.

Key Responsibilities

Customer Engagement & Solution Discovery

• Work directly with customer stakeholders to understand business challenges and identify AI transformation opportunities.
• Translate business requirements into technical solutions and implementation roadmaps.
• Conduct workshops, discovery sessions, architecture reviews, and solution demonstrations.
• Act as a trusted advisor to business and technology leadership.

AI Platform Deployment

• Lead deployment and adoption of Azure AI Platform capabilities.
• Configure and operationalize Azure OpenAI, AI Gateway, Agentic AI frameworks, and enterprise integrations.
• Build reusable accelerators, deployment patterns, and implementation playbooks.
• Support onboarding of business units and engineering teams.

Solution Engineering

• Develop custom integrations, APIs, workflows, and automation solutions.
• Build proof-of-concepts (PoCs), MVPs, and production-ready solutions.
• Integrate AI services with Jira, ServiceNow, GitLab, Azure DevOps, Confluence, and enterprise applications.
• Customize agentic workflows and orchestration frameworks to meet client-specific requirements.

AI Transformation Enablement

• Drive adoption of AI-powered SDLC capabilities.
• Enable use cases such as:
• Requirements-to-Code Automation
• Test Automation
• Developer Productivity Acceleration
• Intelligent Incident Management
• Knowledge Assistants and Copilots
• AI-Driven Operations (AIOps)

Platform Observability & AIOps

• Implement monitoring, observability, and operational excellence practices.
• Define KPIs and adoption metrics.
• Troubleshoot production platform issues and partner with engineering teams on remediation.
• Leverage AIOps capabilities to improve reliability, performance, and operational efficiency.

Product Feedback & Engineering Collaboration

• Gather customer feedback and communicate requirements to product engineering teams.
• Identify gaps, enhancement opportunities, and platform scalability requirements.
• Contribute to product roadmap discussions and feature prioritization.
• Create reference architectures and best practices.

Executive Communication

• Present solution architecture, adoption progress, ROI metrics, and transformation outcomes to customer leadership.
• Prepare executive-ready presentations, architecture documents, and implementation updates.
• Serve as a key liaison between customer stakeholders, engineering teams, and program leadership.

Required Skills

Technical Skills

• Azure Cloud
• Azure OpenAI
• Azure AI Foundry
• Azure API Management
• Kubernetes / AKS
• Microservices Architecture
• REST APIs
• Python, C#, Java, or Node.js
• GitHub / GitLab
• CI/CD Pipelines
• Infrastructure as Code (Terraform, Bicep)

AI & Agentic Engineering

• Generative AI
• LLM Integration
• Prompt Engineering
• RAG Architectures
• AI Agents and Workflows
• MCP Concepts
• Vector Databases
• AI Governance

AIOps & Observability

• Azure Monitor
• Application Insights
• OpenTelemetry
• Logging & Telemetry
• Incident Management
• Root Cause Analysis
• Automation & Self-Healing Concepts