> Markdown version of [/jobs/ext/833026-forward-deployed-engineer](https://www.wearedevelopers.com/jobs/ext/833026-forward-deployed-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Forward Deployed Engineer - **Company:** Blue Orange Digital - **Location:** New York, NY, United States (Remote available) - **Experience:** Experienced - **Salary:** $150,000.0 - $185,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Spreadsheets, Databases, Data Systems, Data Warehousing, Issue Tracking Systems, Python (Programming Language), Routing, Operational Databases, Regression Testing, Standard Sql, Azure Machine Learning, Software Engineering, Management of Software Versions, Data Logging, Large Language Models, Multi-Agent Systems, Zapier, AI Platforms, Enterprise Integration, Integration Frameworks, Nintex, Operational Systems, Api Design, Restful APIs, Pagination, Webhooks, Data Pipelines - **Published:** June 30, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=18e392b9b05ee662 ## About the Role Do you have experience in Workflow management (operations management method)?, * 3 or more years of software engineering experience building systems that run in production, plus the judgment to know what production-ready means * Hands-on experience building with LLMs: prompting, function calling, structured output, retrieval, and at least one agent or multi-step workflow you took past the prototype stage * Strong integration skills, you have connected real systems through REST APIs, webhooks, SDKs, and databases, and dealt with auth, pagination, rate limits, and flaky upstreams * Proficiency in Python, comfort with async patterns, API design, and the data-pipeline reliability that agentic workflows live or die on * Working knowledge of SQL and data systems, enough to pull what a workflow needs from a client's warehouse or operational database * Real client-facing or cross-functional skill: you can sit with a non-technical stakeholder, draw out how their process works, and earn their trust without talking down to them * Strong written and verbal communication, you can explain why an agent took a wrong turn, or why a manual step should stay manual, to a business owner without losing them * A bias toward shipping, you would rather get a working workflow in front of users this week than perfect an architecture diagram * Production experience with agent frameworks (LangGraph, AutoGen, CrewAI) or building custom orchestration * Experience with RAG systems: chunking, embeddings, vector stores (Pinecone, Weaviate, Qdrant, pgvector), and re-ranking, * Familiarity with LLM evaluation: task-specific metrics, LLM-as-judge, and regression test suites * Background in process mapping, solutions engineering, forward-deployed work, or technical consulting across concurrent engagements * Experience with workflow and integration platforms (Temporal, Airflow, Zapier, n8n) or iPaaS tooling * Exposure to a major cloud and its AI services (AWS Bedrock or SageMaker, GCP Vertex AI, Azure ML) ## Description This is the person we send into a client when the problem is not yet a spec. You sit with their operators, finance leads, and analysts, watch how work actually gets done, and turn messy real-world processes into agentic AI workflows that hold up in production. You are dual-fluent by design. In the morning you map a claims-handling or order-to-cash process with a business owner who has never written a line of code. In the afternoon you wire that process into an agent that calls their CRM, queries their warehouse, and routes exceptions to the right human. You translate in both directions, turning vague business pain into concrete technical scope, and turning technical constraints into options a non-technical stakeholder can decide on. The role is a balanced bridge. Roughly half your time is discovery, facilitation, and trust-building with client teams. The other half is hands-on engineering: building the agent logic, the integrations, and the evaluation that proves it works. You will not stop at a prototype. You ship the workflow, instrument it, and hand over something the client can run and extend., * Embed with client teams to understand how work actually happens, shadowing operators and mapping processes that live in people's heads, spreadsheets, and a dozen disconnected tools * Break down complex business processes into agentic workflows: decompose a goal into steps, decide what an agent automates versus where a human stays in the loop, and design the tool-use and decision logic to match * Build production-grade agentic workflows using frameworks like LangGraph and LangChain, including tool calling, memory, routing, retries, and clean failure recovery * Build integrations to the data and operational systems that workflows depend on: CRMs, ERPs, ticketing systems, data warehouses, internal APIs, and document stores, handling auth, rate limits, and reliability * Enable core platform capabilities that the whole engagement reuses: retrieval over client documents, structured output and function calling, prompt versioning, logging, and observability * Design evaluation and guardrails that make a workflow trustworthy in an enterprise setting: task-specific evals, regression checks, PII handling, and human review steps where the stakes demand them * Run working sessions and demos that keep business stakeholders bought in, translating technical tradeoffs into clear decisions about cost, risk, and timeline * Document what you build and hand it over cleanly, so client teams can operate and extend the workflows after the engagement * Bring patterns back to BOD, turning what worked on one engagement into reusable building blocks for the next ## Related Videos - [HR ROBO SAPIENS: Decoding AI Agents and Workflow Automation for Modern Recruitment](https://www.wearedevelopers.com/videos/1470-hr-robo-sapiens-decoding-ai-agents-and-workflow-automation-for-modern-recruitment) - [Building a Multi-Agent Orchestration Engine That Actually Follows the Rules](https://www.wearedevelopers.com/videos/100159-building-a-multi-agent-orchestration-engine-that-actually-follows-the-rules) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Rethinking Workflows in the Agentic Era](https://www.wearedevelopers.com/videos/1540-rethinking-workflows-in-the-agentic-era) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Create a Programmatic SEO Project Using Next.js and Static Site Generation](https://www.wearedevelopers.com/videos/449-create-a-programmatic-seo-project-using-next-js-and-static-site-generation) ## Related Articles - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs)