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ai2ioPosted 28 days ago

Manager, Software Engineering - AI Tools

United States
Full-time
Remote
8+ years
Competitive
Posted 28 days ago

AI2IO is hiring a Manager, Software Engineering to lead the people and delivery environment for its AI platform and agent ecosystem. The role develops engineering talent and team capability, supports sustainable delivery, and helps translate AI strategy into clear organizational and team context.

Information Technology & AITechnical LeadershipEngineering Manager
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Posted
28 days ago
Source
ats.rippling.com
Sector
Information Technology & AI

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Responsibilities

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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

Qualifications

  • 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 recommendations for specialized teams
  • Sufficient software-engineering foundation to assess application architecture, APIs, data, identity, security, testing, CI/CD, observability, dependency, delivery, support, and operational risk without directing implementation
  • Experience supporting responsible AI, data-governance, access-control, evaluation, observability, or model-risk practices in a growing organization
  • Ability to communicate complex AI and data concepts to AI specialists, mainstream software teams, product and delivery partners, and leadership
  • Experience managing distributed or remote teams and improving team practices, documentation, and leadership continuity
  • Excellent interpersonal, written, verbal, coaching, facilitation, conflict-resolution, and organizational judgment
  • 8+ years of professional software engineering, AI engineering, data engineering, data science, or closely related technical-delivery experience, including at least 2 years of formal people management within a software or technology organization

Preferred

  • Experience managing teams that build production AI platforms, agent frameworks, knowledge graphs, semantic layers, retrieval systems, or AI-enabled enterprise applications
  • Hands-on background in AI architecture, LLM application development, data science, data engineering, ontology or taxonomy design, graph technologies, or semantic engineering
  • Experience with Azure AI Foundry, Azure OpenAI, Semantic Kernel, Azure AI Search, Python agent frameworks, Microsoft Azure, C#/.NET, or adjacent enterprise AI platforms
  • Experience applying AI to AEC, commissioning, engineering design, proposal development, document-intensive workflows, or domain-specific business processes
  • Experience developing Technical Leads, AI specialists, data professionals, or emerging technical leaders
  • Relevant management, coaching, Azure AI, data, architecture, Agile, or project-management certifications
  • Bachelor's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, Information Systems, Electrical Engineering, Mathematics, or a materially related field, or equivalent experience preferred
  • .NET
  • AI for AEC

Required skills

Agent SystemsAzure AI FoundryAzure AI SearchAzure OpenAICoachingData GovernanceData ScienceProduction AI/LLM PlatformsRetrieval SystemsSemantic EngineeringSemantic Kernel

Full job description

AI2IO is hiring a Manager, Software Engineering to lead the people and delivery environment for its AI platform and agent ecosystem. The role develops engineering talent and team capability, supports sustainable delivery, and helps translate AI strategy into clear organizational and team context.

Responsibilities

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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

Preferred

  • Experience managing teams that build production AI platforms, agent frameworks, knowledge graphs, semantic layers, retrieval systems, or AI-enabled enterprise applications
  • Hands-on background in AI architecture, LLM application development, data science, data engineering, ontology or taxonomy design, graph technologies, or semantic engineering
  • Experience with Azure AI Foundry, Azure OpenAI, Semantic Kernel, Azure AI Search, Python agent frameworks, Microsoft Azure, C#/.NET, or adjacent enterprise AI platforms
  • Experience applying AI to AEC, commissioning, engineering design, proposal development, document-intensive workflows, or domain-specific business processes
  • Experience developing Technical Leads, AI specialists, data professionals, or emerging technical leaders
  • Relevant management, coaching, Azure AI, data, architecture, Agile, or project-management certifications
  • Bachelor's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, Information Systems, Electrical Engineering, Mathematics, or a materially related field, or equivalent experience preferred
  • Production AI Platforms
  • Agent Frameworks
  • Semantic Layers
  • Retrieval Systems
  • Enterprise AI Applications
  • AI Architecture
  • LLM Application Development
  • Ontology Design
  • Taxonomy Design
  • Graph Technologies
  • Azure AI Foundry
  • Azure OpenAI
  • Semantic Kernel
  • Azure AI Search
  • Python Agent Frameworks
  • Microsoft Azure
  • C#
  • .NET
  • Enterprise AI Platforms
  • AI for AEC
  • Commissioning
  • Engineering Design
  • Proposal Development
  • Document-Intensive Workflows
  • Domain-Specific Business Processes
  • Azure AI Certifications
  • Data Certifications
  • Architecture Certifications
  • Agile Certifications
  • Project Management Certifications
  • Computer Science
  • Artificial Intelligence
  • Information Systems
  • Electrical Engineering
  • Mathematics
  • Team Leadership
  • Talent Development

Benefits

  • Choice of comprehensive medical plans (including two PPO-style plans and a HDHP w/ HSA option)
  • Flex spending accounts (FSA)
  • Dental and vision plans
  • Comprehensive medical, dental and vision benefits extended to spouse / domestic partner and dependent children up to age 26
  • 401k with company match and self-directed brokerage account option
  • PTO including additional paid time off during the last week of the year
  • Company paid life insurance coverage for employees and their eligible dependents
  • Short and long-term disability, AD&D coverage
  • Professional development opportunities, tuition reimbursement and professional licensing assistance
  • Paid parental leave after one year of employment
  • Remote - work virtually from anywhere in the United States

Company

  • Custom software, integrations, automation, and AI solutions for businesses
  • https://www.ai2io.co/
  • 11-50 employees

Qualifications

  • 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 recommendations for specialized teams
  • Sufficient software-engineering foundation to assess application architecture, APIs, data, identity, security, testing, CI/CD, observability, dependency, delivery, support, and operational risk without directing implementation
  • Experience supporting responsible AI, data-governance, access-control, evaluation, observability, or model-risk practices in a growing organization
  • Ability to communicate complex AI and data concepts to AI specialists, mainstream software teams, product and delivery partners, and leadership
  • Experience managing distributed or remote teams and improving team practices, documentation, and leadership continuity
  • Excellent interpersonal, written, verbal, coaching, facilitation, conflict-resolution, and organizational judgment
  • 8+ years of professional software engineering, AI engineering, data engineering, data science, or closely related technical-delivery experience, including at least 2 years of formal people management within a software or technology organization
  • Experience managing teams that build production AI platforms, agent frameworks, knowledge graphs, semantic layers, retrieval systems, or AI-enabled enterprise applications
  • Hands-on background in AI architecture, LLM application development, data science, data engineering, ontology or taxonomy design, graph technologies, or semantic engineering
  • Experience with Azure AI Foundry, Azure OpenAI, Semantic Kernel, Azure AI Search, Python agent frameworks, Microsoft Azure, C#/.NET, or adjacent enterprise AI platforms
  • Experience applying AI to AEC, commissioning, engineering design, proposal development, document-intensive workflows, or domain-specific business processes
  • Experience developing Technical Leads, AI specialists, data professionals, or emerging technical leaders
  • Relevant management, coaching, Azure AI, data, architecture, Agile, or project-management certifications
  • Bachelor's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, Information Systems, Electrical Engineering, Mathematics, or a materially related field, or equivalent experience preferred
  • 8+ years of professional software engineering, AI engineering, data engineering, data science, or closely related technical-delivery experience, including at least 2 years of formal people management within a software or technology organization.

Required skills

  • Agent Systems, Azure AI Foundry, Azure AI Search, Azure OpenAI, Coaching, Data Governance, Data Science, Knowledge Graphs, Production AI/LLM Platforms, Retrieval Systems, Semantic Engineering, Semantic Kernel

Experience

  • 8+ years

Job details

Employment type

Full-time

Seniority level

8+ years

Sector · Sub-sector

Information Technology & AI · Technical Leadership

Date posted

Oct 16, 2026

Work mode

Remote

Job source

ats.rippling.com

Company

ai2io

Information Technology & AI

Custom software, integrations, automation, and AI solutions for businesses

  • United States
  • Information Technology & AI
  • 11-50 employees
  • ai2io.co

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