{"id":2808820,"url":"https://alion.io/job/ai2io-manager-software-engineering-ai-tools","title":"Manager, Software Engineering - AI Tools","company":{"id":4434527,"name":"ai2io","domain":"ai2io.co","url":"https://alion.io/company/ai2io","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":122000,"max_usd":265000,"period":"year","method":"global_role_seniority_cell","sample_n":1199},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":".NET","optional":false},{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"C#","optional":false},{"name":"CI/CD","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"Semantic Kernel","optional":false}],"status":"live","first_seen_at":"2026-10-18T06:25:04Z","employer_posted_date":null,"last_verified_at":"2026-10-18T06:25:04Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"AI2IO helps organizations navigate the complex landscape of technology solutions, from foundational IT services and system support to advanced software integration, automation, and cutting-edge AI implementations. Our expertise spans IT infrastructure management, custom software development, seamless system integrations, and optimization of low-code business automation, empowering clients to maximize their existing technology investments.\n\nJoin us and be part of a team where your voice matters, your work makes an impact, and your growth is a shared priority.\n\nPosition Title\n\nManager, Software Engineering\nPosition Location\n\nRemote - work virtually from anywhere in the United States\n\nReports To\n\nDirector, Software Engineering\nJob Summary\n\nAI2IO is seeking a Manager, Software Engineering to lead the people, capability, and operating environment behind a growing AI platform and agent ecosystem serving architecture, engineering, construction, commissioning, design, proposal, and related business workflows. The team is building governed AI agents, semantic and knowledge-graph capabilities, retrieval and data pipelines, and shared platform services informed by data available within the QCS Ecosystem.\n\nThis is a formal people-management role for all engineers and AI Engineer Technical Leads on the AI Tools team. Candidates must bring demonstrated professional experience leading, managing, architecting, or delivering production AI or LLM platforms or applications, with broad credibility and demonstrated depth across several relevant AI disciplines. The role develops people, builds workforce capability, improves team clarity, and helps translate strategy and architecture into context the team and broader organization can understand without replacing Technical Lead, architecture, product, analysis, or delivery authority.\n\nEssential Functions\n\nThis role grows from complete ownership of people leadership and the AI Tools delivery environment toward broader workforce strategy, specialized capability development, management-system improvement, and organizational leadership across a maturing AI engineering function. As the organization grows, expand impact through multi-team capability planning, development of technical and people leaders, management-system improvement, workforce strategy, and resolution of organizational challenges beyond the assigned specialty.\n\nLead and Develop Engineers\n\nServe as the formal people manager for all engineers and Technical Leads assigned to the specialty team through regular one-on-ones, clear expectations, timely feedback, performance reviews, professional-development planning, and credible support for every direct report.\n\nProvide recognition and development feedback throughout the year and address sustained performance concerns through clear expectations, documented support, follow-through, and appropriate organizational processes.\n\nConnect individual goals to meaningful assignments, coaching, learning, mentoring, and increasing ownership without promising advancement before readiness is demonstrated.\n\nPartner with Technical Leads to understand technical contribution and development needs while retaining accountability for career evaluation, formal performance decisions, employee relations, and team health.\n\nBuild Team Capability and Leadership Depth\n\nMaintain a current view of capacity, critical skills, experience distribution, key-person dependencies, succession risk, and staffing needs.\n\nIdentify capability gaps and coordinate learning, pairing, assignments, mentoring, hiring, and onboarding plans.\n\nDevelop future Technical Leads and management candidates through scoped assignments, calibrated feedback, explicit readiness criteria, and partnership with current leaders.\n\nReduce leadership and knowledge-continuity risk through distributed ownership, documentation, coverage, and succession options.\n\nOwn the Sustainable Delivery Environment\n\nEnsure teams have sufficient staffing, capability, role clarity, decision pathways, tools, and organizational support to deliver sustainably.\n\nPartner with Technical Leads and Project Managers on delivery health, risk, dependencies, capacity, and recovery without replacing technical or project-management ownership.\n\nEnsure material delivery problems receive appropriate management intervention, including staffing changes, escalation, cross-team negotiation, or leadership decisions.\n\nReview recurring delays, incidents, rework, quality concerns, support burdens, and workload imbalance to identify organizational or capability improvements.\n\nProtect teams from sustained overload and commitments incompatible with available capacity.\n\nTranslate AI Strategy Into Clear Team Context\n\nPartner with Director, AI Strategy and Enablement, Product Owners, Business Analysts, Project Managers, architecture contributors, and AI Engineer Technical Leads to translate strategic and architectural direction into objectives, decision context, capability needs, dependencies, risks, and intake-ready work.\n\nParticipate regularly in developer discussions and intake so AI specialists, mainstream software engineers, delivery partners, and leadership share a common understanding of goals, constraints, outcomes, and unresolved decisions.\n\nHelp Business Analysts and Product Owners clarify requirements and stories without taking over product ownership, analysis, acceptance criteria, or backlog priority.\n\nHelp technical leaders explain architectural and implementation direction to non-AI engineers and leadership.\n\nImprove tactical documentation, communication rhythms, and decision records when high-level strategy is not yet expressed clearly enough for execution, while leaving architecture approval and technical decomposition with the appropriate leaders.\n\nBuild AI Platform and Data Capability\n\nOwn workforce capability, staffing, development, and training plans spanning AI application engineering, agents, semantic engineering, ontology and taxonomy, data science, knowledge graphs, retrieval, data pipelines, evaluation, responsible AI, data governance, platform operations, and supporting software engineering.\n\nEnsure the team has the mix of engineering, data, semantic, governance, quality, and operational skills required to build and support a governed AI platform and domain-specific agents.\n\nDevelop engineers across specialized AI concerns and the surrounding C#/.NET, Azure, API, identity, security, testing, CI/CD, observability, and production-support environment.\n\nReduce single-person dependencies in platform architecture, knowledge-graph pipelines, semantic models, agent workflows, data governance, and operations.\n\nSupport Responsible AI, Data Governance, and Platform Maturity\n\nChampion early adoption of responsible AI, data governance, access control, evaluation, observability, model-risk, knowledge-quality, and operational practices as dedicated capabilities mature.\n\nEnsure staffing, training, ownership, and escalation paths exist for model and retrieval quality, sensitive-data handling, lineage, ontology changes, agent behavior, tool permissions, telemetry, incident learning, and support.\n\nPartner with technical leaders and Director, AI Strategy and Enablement to identify governance and platform risks requiring clearer ownership, specialist support, or investment.\n\nBalance research and experimentation with documentation, production readiness, supportability, security, quality, cost awareness, and sustainable delivery.\n\nAs architecture and governance functions mature, help evolve ownership boundaries while remaining accountable for staffing, training, participation, and organizational support.\n\nBuild Healthy, Candid Team Practices\n\nEstablish practices that support candid discussion of risks, uncertainty, mistakes, technical disagreements, workload concerns, and alternative viewpoints without individual blame.\n\nAddress observable patterns such as unresolved conflict, sustained overload, escalation failures, knowledge bottlenecks, or reluctance to surface delivery problems.\n\nMaintain reliable communication rhythms so engineers understand priorities, decisions, role boundaries, and available support.\n\nAddress performance, conduct, and employee-relations concerns through appropriate management processes rather than transferring formal accountability to Technical Leads.\n\nLead Hiring, Selection, and Onboarding\n\nPartner with the Director on workforce planning, role requirements, interview plans, evaluation criteria, and hiring priorities.\n\nLead structured and equitable selection processes, calibrate interviewers, document evidence, and make clear candidate recommendations.\n\nOwn consistent onboarding expectations, including role clarity, access, relationships, learning objectives, 30/60/90-day plans, and ramp check-ins.\n\nEvaluate onboarding effectiveness through independence, role understanding, contribution, and feedback and improve the process based on evidence.\n\nIdentify retention risks early and coordinate appropriate responses.\n\nMaintain Clear Leadership Boundaries\n\nPartner with Technical Leads, who own technical feasibility, estimates, dependencies, execution health, engineering standards, implementation direction, and team-level technical decisions.\n\nPartner with Architects, who own cross-team architecture, technical standards, governance, and long-term technical direction.\n\nPartner with Project Managers, who own the integrated schedule, formal stakeholder communication, scope coordination, project governance, and overall delivery process.\n\nPartner with Product Owners and Business Analysts while preserving their authority for product value, priority, requirements, workflow analysis, and backlog readiness.\n\nUse technical and domain fluency to understand risk, ask informed questions, clarify context, develop people, and improve the environment without becoming the default Technical Lead, Architect, Product Owner, Business Analyst, or Project Manager.\n\nImprove Management Systems and Organizational Effectiveness\n\nLead improvements to performance management, career development, hiring, onboarding, workforce planning, team operating practices, and leadership continuity.\n\nIdentify patterns spanning teams and coordinate improvements with peer Managers, Technical Leads, Architects, Project Managers, Product Owners, Business Analysts, and the Director.\n\nContribute to department-level capability planning, organizational design, role definition, career frameworks, cross-team staffing, leadership development, and organizational change.\n\nEvaluate whether adopted practices improve clarity, fairness, staffing effectiveness, internal growth, management consistency, and sustainability.\n\nRepresent capacity, capability, succession, and organizational risk in department planning with concise evidence and decision-ready recommendations.\n\nPerforms Other Related Duties as Assigned\n\nWhat Success Looks Like\n\nDirect reports understand expectations, receive timely feedback, and have credible development plans aligned with software-engineering and specialized AI capability needs.\n\nThe team has sufficient staffing, role clarity, technical leadership, training, and organizational support to deliver and operate the AI platform sustainably.\n\nStrategic and architectural direction becomes understandable objectives, context, risks, dependencies, and intake-ready work without displacing Product Owner, Business Analyst, AI Engineer Technical Lead, architecture, or Project Manager authority.\n\nAI Engineer Technical Leads retain technical authority and receive effective management partnership rather than tactical micromanagement.\n\nResponsible AI, data governance, evaluation, access, observability, knowledge quality, and production-readiness initiatives have visible ownership, staffing, training, and progress.\n\nKey-person risks in agent architecture, semantic engineering, knowledge graphs, data science, governance, and platform operations are reduced.\n\nEngineers outside the specialty...","description_format":"text","description_chars":18425,"description_truncated":true,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["401k plan","Insurance coverage","Life insurance","Parental leave","Professional development"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"},{"name":"Illinois","iso":null,"kind":"city"},{"name":"Washington","iso":null,"kind":"city"},{"name":"New York","iso":null,"kind":"city"},{"name":"District of Columbia","iso":null,"kind":"city"},{"name":"Colorado","iso":null,"kind":"city"},{"name":"California","iso":null,"kind":"city"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Data & 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