{"id":889664,"url":"https://alion.io/job/greenlightconsulting-forward-deployed-engineer-ai-practice","title":"Forward Deployed Engineer (AI Practice)","company":{"id":674226,"name":"Greenlight Consulting","domain":"greenlightconsulting.com","url":"https://alion.io/company/greenlightconsulting","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-26T05:45:00Z"}},"role":"Solutions","role_family":"Solutions","seniority":"middle","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":90000,"max":130000,"currency":"CAD","period":"year","gross":null,"usd_annual":91969},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Chain-of-Thought","optional":false},{"name":"Claude","optional":false},{"name":"Copilot","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"GraphQL","optional":false},{"name":"Jira","optional":false},{"name":"LangSmith","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Node JS","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"ServiceNow","optional":false},{"name":"SharePoint","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"JavaScript","optional":true}],"status":"live","first_seen_at":"2026-07-07T18:03:28Z","employer_posted_date":"2026-07-07","last_verified_at":"2026-09-26T17:55:25Z","board_verified":true,"closed_at":null,"days_open":81,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":81},"description":"Greenlight helps organizations solve complex business challenges through intelligent automation, agentic AI, and custom technology solutions.\nOur teams work directly with clients to understand their operations, identify opportunities, and rapidly build solutions that create measurable business value. We combine deep consulting expertise with hands-on engineering to bridge the gap between strategy and execution.\nWe’re building a future where consultants and engineers work alongside AI to deliver faster outcomes, stronger businesses, and transformative customer experiences. Anthropic’s Claude is embedded across how we design, build, and validate solutions - and this role is at the technical frontier of that capability.\n\nWhat makes a star at Greenlight?\n· Builder's instinct, architect's discipline\n· The most technically credible person in the room\n· AI-native at depth - you know how LLMs fail and build around it\n· Client-embedded, not arm's length\n· Commercially aware - scope, economics, outcomes\nThe Role\nThe Forward Deployed Engineer is Greenlight’s most senior technical delivery role. You design, build, and operationalize AI agent workflows and intelligent automation solutions - embedded directly in client environments, working alongside their teams to deliver production-grade solutions that create measurable business outcomes.\nThis is not a back-office engineering role. You will be in client discovery sessions, presenting architectures to IT leadership, deploying AI agents against real enterprise systems, and building the reusable skills and accelerators that make Greenlight faster and more differentiated on every subsequent engagement. You are expected to be the most technically credible person in the room - combining deep AI fluency with the consulting presence of a senior practitioner.\nYou work at the frontier of agentic AI - designing and building Claude-powered solutions, connecting AI agents to enterprise systems through MCP integrations, and producing the technical documentation that the delivery team and the client can both stand behind. The platform is Anthropic Claude. The problem space is complex, regulated, and high-stakes. The bar is production-grade.\nAt a Glance\n\nReports To\n\nAI Practice Lead\n\nWorks Closely With\n\nPre-Sales SE, Automation Business Consultant, AI Delivery Engagement Manager\n\nClient Interaction\n\nYes - C-suite, IT architects, operations leaders, technical teams\n\nTravel Requirement\n\nRegular client travel required - discovery, workshops, POC delivery, go-live\n\nPlatform Focus\n\nAnthropic Claude (Cowork + Skills), MCP, Python / Node.js, REST APIs\n\nSeniority\n\nIntermediate 3-5 year’s experience\n\nLocation\n\nOnshore Canada - Toronto preferred\n\nEngagement Type\n\nHybrid - onsite client-facing with remote delivery phases\n\nWhat You’ll Do\nSolution Architecture & Technical Discovery\nLead technical discovery workshops with C-suite, IT architects, and operations leaders - identifying high-value AI automation opportunities and translating complex operational workflows into executable solution architectures\nDesign end-to-end AI agent architectures using Anthropic’s Claude platform (Cowork and Skills framework), aligned to client infrastructure, security requirements, and compliance constraints\nTranslate business requirements into agent-based automation blueprints: what the AI owns, what stays human, how exceptions route, and how the two coordinate\nAssess the right solution architecture for each use case - knowing when a Claude agent is the answer, when a procode integration is cleaner, and when a simpler rules-based approach is more appropriate than AI\nPresent technical architectures to executive and non-technical audiences with clarity, confidence, and the credibility that comes from having built things like this before\nAI Agent Build & Delivery\nEngineer production-ready AI agent workflows using Claude Cowork and the Skills framework - built to a standard that holds up in regulated, enterprise production environments\nDevelop and configure Claude Skills: structured instruction sets, templates, validation rules, and reference documents that encode client-specific processes and quality standards into repeatable AI agent behaviour\nBuild and maintain MCP connectors that integrate AI agents with enterprise systems - CRMs, ERPs, document management platforms, core banking systems, and custom APIs - handling authentication, data mapping, error handling, and audit requirements\nImplement advanced LLM patterns where required: retrieval-augmented generation (RAG), chain-of-thought reasoning, tool use, multi-agent orchestration, and structured output validation\nConduct output validation, performance tuning, and safety testing of deployed AI agents before client go-live - quality is non-negotiable when outputs go to regulators or clients\nProcode & Integration Engineering\nWrite production-quality Python or Node.js to extend platform capability - data pipelines, transformation logic, webhook handlers, API wrappers, and back-end components that sit outside the automation itself\nBuild, test, and maintain integrations with enterprise platforms (Salesforce, Jira, DocuSign, SharePoint, ServiceNow, and others) using REST, GraphQL, OAuth 2.0, and enterprise authentication patterns\nDesign and implement monitoring, logging, and observability into deployed solutions so operations teams can diagnose issues without requiring an engineer in the room\nApply cloud platform fundamentals (AWS, Azure, or GCP) to deployment, scaling, and infrastructure decisions where the engagement requires it\nAccelerators & Practice Development\nBuild and continuously refine reusable Skills libraries, automation templates, and reference architectures that make every subsequent Greenlight engagement faster and more defensible\nContribute to pre-sales activities: technical demos, POC builds, RFP responses, and solution estimations - your hands-on credibility is a direct input to Greenlight’s win rate\nProduce high-quality technical documentation - solution design documents, architecture diagrams, runbooks, and post-deployment reports - written to a standard the client’s IT team can maintain\nIdentify account expansion opportunities within active engagements - additional use cases, adjacent processes, and follow-on automation candidates - and surface them to the AI Delivery Engagement Manager\nMentor Automation Engineers and client technical teams on AI agent patterns, LLM integration best practices, and platform capabilities\nClient Engagement & Consulting Delivery\nOperate with high autonomy inside client environments - navigating ambiguity, defining structure where none exists, and delivering without requiring hand-holding from the practice lead\nManage scope, timelines, and deliverables across concurrent client engagements - you know when to flag a risk and when to absorb it\nBuild trusted-advisor relationships with client technical teams - the goal is to be the person they call when they have an AI or automation problem, not just the vendor who delivered the last project\nDeliver working solutions within 2-4 week sprint cycles - you build and show, you don’t slide and tell\nWhat We’re Looking For\nSkills & Competencies\nTechnical depth with consulting presence - credible with a CTO and a COO in the same meeting\nAI-native - you understand how LLMs behave under pressure, where they fail, and how to build reliable systems around them\nBuilder's bias - working software over slide decks, every time\nHigh autonomy - you define structure where none exists without needing direction\nCommercially aware - scope, delivery economics, and what it costs when technical decisions go sideways\nClear communicator - architecture decisions and trade-offs explained in language both business and IT can act on\nTechnical Familiarity\nLLM platforms - Anthropic Claude (primary), Microsoft Copilot, Google Gemini, OpenAI; hands-on with APIs, agent frameworks, and production deployment across at least one\nClaude Skills framework and Cowork - structured instruction design, output validation, skill packaging\nMCP (Model Context Protocol) - connector design, enterprise system integration, authentication flows\nPython and/or Node.js - production-grade, testable, maintainable code\nREST and GraphQL APIs - authentication, error handling, rate limiting, webhooks\nCloud platforms - AWS, Azure, or GCP; deployment, containers, infrastructure basics\nLLM evaluation and observability - LangSmith, Weights & Biases, or equivalent\nWhat Great Looks Like in This Role\n\nThe standard we are hiring to:\nThe best FDEs we have worked with walk into a client environment and within a week they know more about the client’s process than the client’s own team has documented. They build a working POC in a few weeks that makes the client stop asking whether it’s possible and start asking when it can go to production. They write code that the next engineer can understand without a two-hour briefing. They know when the LLM is going to fail before it does, and they’ve already built the fallback. And they leave every engagement with a client technical team that is more capable than when they arrived - because that is what makes clients come back.\n\nAI in Our Hiring Process\nWe use AI tools to support parts of our recruitment - organizing applications and flagging relevant experience. These tools inform our process, they don’t drive it. Every hiring decision is made by our team, full stop.\nCompensation\nThe expected salary range for this role is $90,000 - $130,000 CAD annually, based on experience and qualifications.","description_format":"text","description_chars":9568,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Canada","iso":"CA","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","AI Agents"],"lifecycle":[{"event":"open","at":"2026-09-14T06:11:37Z"}],"liveness":{"score":19,"band":"cold","label":"Long shot","p_open":1,"p_active":0.537,"p_room":0.36,"age_days":80,"expected_fill_days":43,"reasons":["conf:7","win:tail","crowd:"],"computed_at":"2026-09-26T05:45:00Z"},"pay":{"stated_usd_annual":91969,"is_top_pay":false},"html_url":"https://alion.io/job/greenlightconsulting-forward-deployed-engineer-ai-practice","json_url":"https://alion.io/job/greenlightconsulting-forward-deployed-engineer-ai-practice.json","meta":{"generated_at":"2026-09-27T00:36:46Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":518,"day_limit":5000,"remaining_today":4482,"minute_limit":60,"resets_at":"2026-09-28T00:00:00Z"}}}