Manager, Software Engineering - AI Tools

Position Title

Manager, Software Engineering

Position Location

Remote - work virtually from anywhere in the United States

Reports To

Director, Software Engineering

Job Summary

AI2IO 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.

This 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.

Essential Functions

This 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.

Lead and Develop Engineers

• Serve 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.

• Provide recognition and development feedback throughout the year and address sustained performance concerns through clear expectations, documented support, follow-through, and appropriate organizational processes.

• Connect individual goals to meaningful assignments, coaching, learning, mentoring, and increasing ownership without promising advancement before readiness is demonstrated.

• Partner with Technical Leads to understand technical contribution and development needs while retaining accountability for career evaluation, formal performance decisions, employee relations, and team health.

Build Team Capability and Leadership Depth

• Maintain a current view of capacity, critical skills, experience distribution, key-person dependencies, succession risk, and staffing needs.

• Identify capability gaps and coordinate learning, pairing, assignments, mentoring, hiring, and onboarding plans.

• Develop future Technical Leads and management candidates through scoped assignments, calibrated feedback, explicit readiness criteria, and partnership with current leaders.

• Reduce leadership and knowledge-continuity risk through distributed ownership, documentation, coverage, and succession options.

Own the Sustainable Delivery Environment

• Ensure teams have sufficient staffing, capability, role clarity, decision pathways, tools, and organizational support to deliver sustainably.

• Partner with Technical Leads and Project Managers on delivery health, risk, dependencies, capacity, and recovery without replacing technical or project-management ownership.

• Ensure material delivery problems receive appropriate management intervention, including staffing changes, escalation, cross-team negotiation, or leadership decisions.

• Review recurring delays, incidents, rework, quality concerns, support burdens, and workload imbalance to identify organizational or capability improvements.

• Protect teams from sustained overload and commitments incompatible with available capacity.

Translate AI Strategy Into Clear Team Context

• Partner 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.

• Participate 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.

• Help Business Analysts and Product Owners clarify requirements and stories without taking over product ownership, analysis, acceptance criteria, or backlog priority.

• Help technical leaders explain architectural and implementation direction to non-AI engineers and leadership.

• Improve 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.

Build AI Platform and Data Capability

• Own 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.

• Ensure 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.

• Develop engineers across specialized AI concerns and the surrounding C#/.NET, Azure, API, identity, security, testing, CI/CD, observability, and production-support environment.

• Reduce single-person dependencies in platform architecture, knowledge-graph pipelines, semantic models, agent workflows, data governance, and operations.

Support Responsible AI, Data Governance, and Platform Maturity

• Champion early adoption of responsible AI, data governance, access control, evaluation, observability, model-risk, knowledge-quality, and operational practices as dedicated capabilities mature.

• Ensure 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.

• Partner with technical leaders and Director, AI Strategy and Enablement to identify governance and platform risks requiring clearer ownership, specialist support, or investment.

• Balance research and experimentation with documentation, production readiness, supportability, security, quality, cost awareness, and sustainable delivery.

• As architecture and governance functions mature, help evolve ownership boundaries while remaining accountable for staffing, training, participation, and organizational support.

Build Healthy, Candid Team Practices

• Establish practices that support candid discussion of risks, uncertainty, mistakes, technical disagreements, workload concerns, and alternative viewpoints without individual blame.

• Address observable patterns such as unresolved conflict, sustained overload, escalation failures, knowledge bottlenecks, or reluctance to surface delivery problems.

• Maintain reliable communication rhythms so engineers understand priorities, decisions, role boundaries, and available support.

• Address performance, conduct, and employee-relations concerns through appropriate management processes rather than transferring formal accountability to Technical Leads.

Lead Hiring, Selection, and Onboarding

• Partner with the Director on workforce planning, role requirements, interview plans, evaluation criteria, and hiring priorities.

• Lead structured and equitable selection processes, calibrate interviewers, document evidence, and make clear candidate recommendations.

• Own consistent onboarding expectations, including role clarity, access, relationships, learning objectives, 30/60/90-day plans, and ramp check-ins.

• Evaluate onboarding effectiveness through independence, role understanding, contribution, and feedback and improve the process based on evidence.

• Identify retention risks early and coordinate appropriate responses.

Maintain Clear Leadership Boundaries

• Partner with Technical Leads, who own technical feasibility, estimates, dependencies, execution health, engineering standards, implementation direction, and team-level technical decisions.

• Partner with Architects, who own cross-team architecture, technical standards, governance, and long-term technical direction.

• Partner with Project Managers, who own the integrated schedule, formal stakeholder communication, scope coordination, project governance, and overall delivery process.

• Partner with Product Owners and Business Analysts while preserving their authority for product value, priority, requirements, workflow analysis, and backlog readiness.

• Use 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.

Improve Management Systems and Organizational Effectiveness

• Lead improvements to performance management, career development, hiring, onboarding, workforce planning, team operating practices, and leadership continuity.

• Identify patterns spanning teams and coordinate improvements with peer Managers, Technical Leads, Architects, Project Managers, Product Owners, Business Analysts, and the Director.

• Contribute to department-level capability planning, organizational design, role definition, career frameworks, cross-team staffing, leadership development, and organizational change.

• Evaluate whether adopted practices improve clarity, fairness, staffing effectiveness, internal growth, management consistency, and sustainability.

• Represent capacity, capability, succession, and organizational risk in department planning with concise evidence and decision-ready recommendations.

• Performs Other Related Duties as Assigned

What Success Looks Like

• Direct reports understand expectations, receive timely feedback, and have credible development plans aligned with software-engineering and specialized AI capability needs.

• The team has sufficient staffing, role clarity, technical leadership, training, and organizational support to deliver and operate the AI platform sustainably.

• Strategic 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.

• AI Engineer Technical Leads retain technical authority and receive effective management partnership rather than tactical micromanagement.

• Responsible AI, data governance, evaluation, access, observability, knowledge quality, and production-readiness initiatives have visible ownership, staffing, training, and progress.

• Key-person risks in agent architecture, semantic engineering, knowledge graphs, data science, governance, and platform operations are reduced.

• Engineers outside the specialty and leaders receive clear explanations of AI Tools work, tradeoffs, risks, and outcomes.

• Management practices remain consistent with the broader Software Engineering organization while adapting to experimentation, research uncertainty, and production AI operations.

Position Requirements

• Demonstrated professional experience leading, managing, architecting, or delivering production AI or LLM platforms or applications, with credible fluency across several of the following: agent systems, semantic engineering, retrieval, data science, data engineering, data governance, knowledge graphs, evaluation, responsible AI, and production AI operations.

• Demonstrated ability to translate complex technical or architectural direction into organizational context, capability needs, risks, dependencies, and actionable team understanding.

• Experience managing performance, difficult feedback, recognition, development, reviews, promotions, and sustained performance concerns.

• Experience managing engineers and technical leaders while preserving clear authority for AI Engineer Technical Leads, Architects, Product Owners, Business Analysts, and Project Managers.

• Experience with workforce planning, hiring, skill-gap assessment, onboarding, retention, succession, and staffing