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), LangGraph-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 (LangGraph-based).
• Translate business needs into scalable architectures using:
• Azure AI Services / Azure OpenAI
• LangChain / LangGraph 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)
• LangGraph / LangChain (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
• LangGraph / 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

• LangChain / LangGraph – 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)
• DevOps, 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.