Forward Deployed Engineer Job Dallas area,Texas USA,IT/Tech

Role:
Forward Deployed Engineer

Role Overview

The Forward Deployed Engineer (FDE) operates as an end-to-end owner of intelligent automation and AI-driven initiatives. This is a highly autonomous role requiring a blend of technical expertise, functional understanding, and stakeholder management.

The FDE works directly with business teams to design and deliver scalable solutions leveraging AI/ML, Large Language Models (LLMs), Lang Graph-based orchestration, Python, and Azure cloud platforms-without reliance on separate BA, architect, or developer roles.

Key Responsibilities

1. Business Engagement & Requirement Gathering

Engage directly with business stakeholders to capture and refine requirements.
Identify and assess automation and AI opportunities (e.g., GenAI, workflow automation, copilots).
Perform feasibility analysis for implementing AI/ML or LLM-based solutions.
Build strong relationships and act as a trusted technical advisor to business teams.
Represent the engineering team in stakeholder discussions and strategy sessions.

2. Solution Design & Documentation

Develop Process Design Documents (PDDs) and solution architecture designs.
Create detailed AI/ML use cases, including LLM-powered workflows and agentic systems (Lang Graph-based).
Translate business needs into scalable architectures using:
- Azure AI Services / Azure OpenAI
- Lang Chain / Lang Graph workflows
- Python-based microservices
Define data flows, APIs, and integration strategies across enterprise systems.

3. Costing, Feasibility & ROI Analysis

Estimate implementation effort, infrastructure costs, and licensing requirements.
Evaluate ROI of automation and GenAI use cases.
Recommend optimal architecture considering cost, scalability, and performance.
Support budgeting decisions with data-driven insights.

4. End-to-End Development Ownership

Own complete Software Development Lifecycle (SDLC):
- Design → Development → Testing → Deployment → Support
Develop solutions using:
- Python (core development, APIs, data processing)
- LLMs (Azure OpenAI, prompt engineering, embeddings, RAG)
- Lang Graph / Lang Chain (agent orchestration, multi-step workflows)
Build intelligent agents, copilots, and automation pipelines.
Deliver demos and iterative updates to stakeholders.

5. Infrastructure & Platform Enablement

Set up and manage cloud infrastructure in Microsoft Azure, including:
- Azure OpenAI / Cognitive Services
- Azure Functions / App Services
- Azure Data Factory / Synapse
- Storage & databases (SQL, Cosmos DB, Data Lake)
Implement CI/CD pipelines, monitoring, and logging.
Collaborate with platform teams for enterprise-grade deployments.
Identify and address gaps in tools, frameworks, and environments.

6. Stakeholder Communication & Delivery Management

Lead regular updates, sprint demos, and solution walkthroughs.
Manage User Acceptance Testing (UAT) cycles and feedback loops.
Ensure timely delivery aligned with business expectations.
Take full ownership of delivery timelines and quality.

7. Continuous Innovation & Learning

Stay current with evolving technologies:
- Generative AI, Copilot ecosystems, LLMs
- Lang Graph / Agent frameworks
- Azure AI advancements
Identify opportunities to enhance processes through automation and AI.
Proactively introduce innovative solutions and best practices.

Technical Skills Required

Core Technologies

Programming: Python (mandatory), APIs, data processing
- AI/ML:ML fundamentals (training, evaluation, model lifecycle)
- NLP and Generative AI concepts
- LLMs & GenAI:Azure OpenAI / OpenAI APIs
- Prompt engineering, RAG, embeddings
- Fine-tuning & evaluation approaches

Frameworks & Tools

Lang Chain / Lang Graph - agent orchestration and workflow automation
Vector databases (e.g., FAISS, Azure AI Search)
REST APIs, FastAPI

Cloud (Azure)

Azure OpenAI, Cognitive Services
Azure Data Factory / Synapse Analytics
Azure Functions / App Services
Data storage (Blob, Data Lake, SQL)
Dev Ops, CI/CD pipelines

Ideal Candidate Profile

Strong blend of technical, functional, and business acumen
Proven ability to own end-to-end solution delivery independently
Hands-on experience with LLMs, AI/ML, and cloud-native development
Excellent communication and stakeholder management skills
Experience building intelligent automation or copilot-like solutions
Self-driven problem solver with a proactive mindset

Additional Expectations

Ability to multitask across multiple initiatives
Balance competing priorities while maintaining delivery quality
Adapt quickly in dynamic, fast-paced environments
High accountability and ownership mindset

Summary

This role combines responsibilities typically distributed across Business Analyst, Solution Architect, and Developer roles, making it ideal for a highly skilled, adaptable engineer capable of driving AI-powered transformation initiatives end-to-end.