Forward Deployed Engineer
Anblicks
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
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 .
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- LangChain / LangGraph - agent orchestration and workflow automation
- Vector databases (e.g., FAISS, Azure AI Search)
- REST APIs, FastAPI
- Azure OpenAI, Cognitive Services
- Azure Data Factory / Synapse Analytics
- Azure Functions / App Services
- Data storage (Blob, Data Lake, SQL)
- DevOps, CI/CD pipelines
- 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
- 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 .
Vacancy posted 1 day ago
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