{"id":2604551,"url":"https://alion.io/job/proactiviti-ai-orchestrator-workflow-architect","title":"AI Orchestrator & Workflow Architect","company":{"id":4423600,"name":"Proactiviti","domain":"proactiviti.com","url":"https://alion.io/company/proactiviti","size_band":null,"is_staffing_agency":false,"employer_type":"staffing","is_intermediary":false,"listed_via":null,"ats_vendor":"Manatal","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"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":"Anthropic","optional":false},{"name":"AutoGen","optional":false},{"name":"ChatGPT","optional":false},{"name":"Claude","optional":false},{"name":"Claude Code","optional":false},{"name":"Context Engineering","optional":false},{"name":"Copilot","optional":false},{"name":"CrewAI","optional":false},{"name":"Cursor","optional":false},{"name":"Embeddings","optional":false},{"name":"Flowise","optional":false},{"name":"Function Calling","optional":false},{"name":"Gemini","optional":false},{"name":"GraphQL","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"JavaScript","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Multimodal AI","optional":false},{"name":"n8n","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Replit","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TypeScript","optional":false},{"name":"Zapier","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"GCP","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"LLM","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Vercel","optional":true}],"status":"live","first_seen_at":"2026-10-15T09:17:07Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-22T09:35:53Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":7},"description":"We are seeking an execution-focused AI Orchestrator & Workflow Architect to design, build, and deploy end-to-end AI-enabled business systems. This role is intended for a highly adaptable technology professional who understands how to combine generative AI platforms, autonomous agents, software applications, data sources, APIs, and workflow automation tools into practical solutions that improve how an organization operates.\nThe successful candidate will not be limited to a single model, framework, or development methodology. They will understand the strengths and limitations of leading commercial and open-source AI ecosystems and will select the right combination of tools for each use case. These may include Claude, ChatGPT, Gemini, GitHub Copilot, Cursor, Codex, v0, Replit, LangChain, CrewAI, Flowise, Make, n8n, and emerging platforms.\nThis is not a traditional machine learning research role, nor is it solely a low-code automation position. It is a hands-on solutions role for someone who can take an ambiguous operational or commercial problem, map it into a structured system, rapidly build a functional solution, and guide that solution into production and adoption.\nThe AI Orchestrator & Workflow Architect will bridge business strategy and technical execution. They will act as a force multiplier across the organization by accelerating experimentation, reducing manual work, connecting fragmented systems, and creating intelligent workflows that can scale responsibly.\nCore Responsibilities\nAI Platform Orchestration and Multi-Tool Strategy\nServe as a central architect for the organization's generative AI and automation stack, selecting the most appropriate models, platforms, development environments, and orchestration tools for each requirement. \nEvaluate and integrate commercial and open-source AI ecosystems, including solutions from OpenAI, Anthropic, Google, Microsoft, and other emerging providers. \nDesign model-agnostic architectures that allow systems to use different models based on accuracy, reasoning capability, speed, privacy, availability, and cost. \nCombine conversational AI, coding assistants, multimodal models, search, structured data tools, automation platforms, and traditional software components into cohesive solutions. \nUse AI-assisted development environments such as GitHub Copilot, Cursor, Codex, Claude Code, v0, and Replit to accelerate prototyping, application development, testing, and documentation. \nMaintain a working understanding of new model releases, agent capabilities, orchestration patterns, and AI development tools, and translate relevant advances into measurable operational improvements. \nEstablish reusable templates, connectors, prompt libraries, workflow components, and implementation standards that enable teams to build AI solutions more quickly and consistently. \nAgentic Workflow and Autonomous System Design\nDesign and build autonomous and semi-autonomous agent systems that can reason through tasks, retrieve information, invoke tools, make bounded decisions, and complete multi-step workflows. \nTranslate complex business processes into structured agent workflows with clearly defined objectives, roles, inputs, outputs, decision points, escalation paths, and completion criteria. \nDevelop multi-agent systems in which specialized agents communicate, delegate work, validate outputs, and coordinate toward a shared business objective. \nImplement memory, context management, state persistence, task queues, retries, and exception-handling mechanisms appropriate to the workflow. \nCreate robust human-in-the-loop checkpoints for approvals, sensitive decisions, ambiguous cases, and high-impact actions. \nBuild validation loops, policy checks, confidence thresholds, deterministic controls, and guardrails that reduce hallucinations and prevent inappropriate autonomous behavior. \nDesign observability and evaluation mechanisms that measure workflow completion, quality, accuracy, latency, cost, user satisfaction, and business impact. \nEnd-to-End AI Solutions Engineering\nTake ownership of AI-enabled solutions from initial problem definition and process discovery through architecture, prototyping, testing, deployment, and user adoption. \nRapidly convert loosely defined business requirements into functional applications, internal tools, automation workflows, dashboards, assistants, or agent-based systems. \nUse a pragmatic mix of low-code, no-code, AI-generated code, and traditional scripting to deliver the right level of speed, maintainability, and scalability. \nBuild user interfaces, lightweight web applications, APIs, integration services, data pipelines, and workflow logic needed to support complete business solutions. \nConnect databases, documents, knowledge repositories, enterprise applications, email, collaboration platforms, CRM systems, service-management platforms, and external APIs to AI-powered workflows. \nMove successful prototypes into stable, secure, and supportable production environments with appropriate documentation, monitoring, testing, and ownership. \nWork with users to refine solutions based on real-world feedback and ensure that tools are adopted, trusted, and embedded into day-to-day operations. \nBusiness Process and Workflow Architecture\nAnalyze existing operational processes to identify repetitive work, decision bottlenecks, fragmented data, manual handoffs, and opportunities for intelligent automation. \nDecompose human workflows into clear algorithmic steps while preserving necessary judgment, context, and governance. \nDetermine which workflow elements should be automated, AI-assisted, deterministic, manually reviewed, or excluded from automation. \nCreate process maps, solution diagrams, data-flow models, agent responsibilities, and functional specifications that make complex systems understandable to business and technical stakeholders. \nDefine measurable outcomes for each solution, including productivity gains, cycle-time reduction, service improvements, revenue impact, cost reduction, or risk mitigation. \nIntegration, Data and Technical Enablement\nDesign and implement integrations using REST APIs, GraphQL, webhooks, event triggers, message queues, database connectors, and platform-specific automation capabilities. \nWork with structured and unstructured data from relational databases, spreadsheets, documents, email, knowledge bases, cloud storage, and third-party systems. \nImplement retrieval-augmented generation, semantic search, embeddings, vector stores, document ingestion, and structured output workflows where appropriate. \nUse Python, JavaScript or TypeScript, SQL, and shell scripting to extend platforms, transform data, troubleshoot integrations, and close gaps that cannot be addressed through no-code tools alone. \nApply secure handling of credentials, authentication, authorization, sensitive data, model access, and external tool permissions. \nCollaborate with software engineering, data, security, IT, operations, and product teams to ensure solutions align with enterprise architecture and governance requirements. \nStakeholder Engagement and Adoption\nPartner with business leaders and functional teams to identify high-value AI use cases and convert operational needs into viable implementation plans. \nLead discovery sessions, process-mapping workshops, technical demonstrations, pilot reviews, and solution handoffs. \nCommunicate complex AI capabilities, limitations, risks, and tradeoffs in clear language for both technical and non-technical audiences. \nSet realistic expectations regarding model reliability, autonomy, data quality, security, implementation effort, and required human oversight. \nDevelop user guidance, operating procedures, training materials, and adoption plans that help teams use new systems effectively. \nAct as an internal advisor on responsible and practical uses of generative AI, automation, and agentic technology. \nRequired Skills and Qualifications\nDemonstrated experience designing and delivering AI-enabled applications, intelligent workflows, automation platforms, or autonomous and semi-autonomous agent systems. \nStrong practical experience with multiple generative AI ecosystems, such as Claude, ChatGPT, Gemini, GitHub Copilot, Cursor, Codex, Claude Code, v0, or comparable tools. \nExperience using orchestration and automation technologies such as LangChain, LangGraph, CrewAI, AutoGen, Flowise, Make, n8n, Zapier, or comparable platforms. \nAbility to map ambiguous and complex business processes into structured workflows, decision trees, system components, and executable logic. \nProficiency in prompt design, context engineering, tool calling, structured outputs, JSON schemas, validation logic, and model instruction design. \nPractical ability to read, assemble, debug, and modify code in Python, JavaScript or TypeScript, and SQL, including code generated through AI-assisted development tools. \nExperience integrating applications through APIs, webhooks, databases, identity services, collaboration platforms, CRM systems, email, and other business applications. \nUnderstanding of software delivery fundamentals, including environments, source control, testing, deployment, logging, monitoring, documentation, and production support. \nStrong product mindset and a demonstrated ability to focus on user outcomes rather than technology experimentation alone. \nExcellent written and verbal communication skills, with the ability to work effectively with executives, operational teams, engineers, and end users. \nExceptional learning agility and a track record of rapidly mastering new technologies, platforms, and delivery approaches. \nAbility to work independently, prioritize competing opportunities, and move from concept to working solution with limited supervision. \nPreferred Qualifications\nBackground in software engineering, solutions architecture, product development, business systems, data engineering, process automation, or technology consulting. \nExperience building customer-facing or enterprise-grade AI products rather than only personal productivity automations or demonstrations. \nFamiliarity with retrieval-augmented generation, vector databases, semantic search, knowledge graphs, model routing, and AI gateways. \nExperience with Model Context Protocol, tool servers, function calling, agent memory, and standardized AI integration patterns. \nExperience deploying applications in AWS, Microsoft Azure, Google Cloud Platform, Vercel, Replit, or comparable cloud and application-hosting environments. \nUnderstanding of AI security and governance topics, including prompt injection, data leakage, excessive agency, access control, auditability, privacy, and responsible AI. \nExperience measuring the operational and financial value of automation initiatives and prioritizing use cases based on impact, complexity, and risk. \nExperience supporting organizational adoption of new tools through documentation, enablement, stakeholder engagement, and change management. \nTechnical Areas of Interest\nLarge language models and multimodal AI \nAI agents and multi-agent orchestration \nClaude, ChatGPT, Gemini, Codex, Claude Code and AI-assisted development environments \nPrompt design, context engineering and structured model outputs \nAgent-to-agent communication, planning, delegation and memory \nHuman-in-the-loop workflows and approval systems \nRetrieval-augmented generation and semantic search \nAPIs, webhooks and enterprise application integration \nLow-code and no-code automation platforms \nPython, JavaScript or TypeScript, SQL and lightweight application development \nWorkflow engines, event-driven systems and business process automation \nAI evaluation, guardrails, observability and performance monitoring \nCloud deployment, security and production operations \nProduct discovery, process mapping and user adoption \nSuccess in This Role\nSuccess will be measured by the candidate's ability to:\nTurn complex or loosely defined business problems into clear, 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