{"id":682468,"url":"https://alion.io/job/flatgigs-forward-deployed-ai-engineer","title":"Forward Deployed AI Engineer","company":{"id":672990,"name":"Flatgigs","domain":"flatgigs.com","url":"https://alion.io/company/flatgigs","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":100,"open_postings":57,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-28T05:59:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Amman, Jordan","Cairo, Egypt","Tunis, Tunisia","Algiers, Algeria"],"countries":["JO","EG","TN","DZ"],"hiring_countries":["EG","JO","TN"],"hiring_countries_total":3,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"Incident Management","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Python","optional":false},{"name":"Slack","optional":false},{"name":"Structured Outputs","optional":false},{"name":"AWS","optional":true}],"status":"live","first_seen_at":"2026-04-28T00:00:00Z","employer_posted_date":"2026-04-28","last_verified_at":"2026-10-29T00:56:35Z","board_verified":true,"closed_at":null,"days_open":184,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":183},"description":"The Role\nThe Forward Deployed AI Engineer will work directly with clients and senior engineers to build AI-powered systems inside real enterprise environments. This role is ideal for a strong junior engineer who wants to work on practical AI systems, enterprise data, agents, retrieval, workflow automation, and context graphs. You will help connect to client systems, process messy real-world data, build prototypes, and turn business problems into working software.\nThe work is hands-on and client-facing:\nUnderstand operational workflows and convert them into technical artifacts.\nConnect to real enterprise systems and data sources.\nBuild context-aware AI agents, retrieval systems, and automation prototypes.\nHelp move the best prototypes toward reliable production deployments.\nRequirements\nBuild context graphs. Help ingest, clean, structure, and connect data from enterprise systems into context graph. This may include structured databases, PDFs, spreadsheets, tickets, CRM data, analytics events, Slack or Teams exports, meeting transcripts, operational workflows, and other internal knowledge sources.\nDevelop AI and agentic workflows. Build AI pipelines and agent-based systems that can reason over enterprise context, identify patterns, surface workflow gaps, and suggest or trigger automations. This may involve LLMs, retrieval systems, structured extraction, tool use, LangGraph-style workflows, and agent harnesses.\nConnect to enterprise systems. Integrate with client infrastructure such as databases, APIs, cloud storage, document repositories, analytics tools, ticketing systems, and internal applications.\nPrototype automation opportunities. Rapidly build proof-of-concepts that show how AI can improve a client's operations - for example, process documentation, workflow discovery, incident management, document extraction, customer journey analysis, or operational decision support.\nTurn messy business problems into software. Work with senior engineers and business stakeholders to translate ambiguous operational problems into technical designs, data models, prompts, pipelines, and deployed applications.\nSupport production deployments. Help build reliable, maintainable systems that can run in production environments, including client cloud environments when needed.\nIdeal Candidate\nWe are looking for a strong junior software engineer who is excited about applied AI and wants to build real systems, not just experiments.\nYou do not need to be an expert in every area below, but you should be curious, technical, and comfortable learning quickly.\nTechnical Background\nStrong Python fundamentals.\nBasic backend development experience • Familiarity with APIs, databases, and cloud services.\nInterest in LLMs, agents, retrieval, structured outputs, and tool-calling.\nFamiliarity with data pipelines and messy real-world datasets.\nExperience with FastAPI, LangChain, LangGraph, vector databases, document processing, or knowledge graphs is a plus.\nAWS experience is a plus.\nExperience with enterprise data, analytics, or workflow automation is a strong plus.\nWhat Makes Someone Successful in This Role\nYou like figuring out how businesses actually operate\nYou are comfortable working with incomplete, messy, or poorly documented data\nYou can move quickly from vague requirements to a working prototype\nYou care about building useful systems, not just impressive demos\nYou communicate clearly with both engineers and non-technical stakeholders\nYou are curious about how AI agents can interact with real tools, data, and workflows\nYou want to learn how to deploy AI into real enterprise environments\nWhy This Role Is Interesting\nYou will work on the frontier of practical enterprise AI: connecting AI agents to real company context and using that context to discover and automate high-value workflows.\nThis is a hands-on engineering role for someone who wants to grow into building production-grade AI systems across data, agents, infrastructure, and enterprise 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