Deployed Engineer (Intelligent Automation)

10+ Years Full-Time 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. 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 REST APIs, FastAPI Azure Functions / App Services Data storage (Blob, Data Lake, SQL) 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 . #J-18808-Ljbffr Anblicks Inc.