Sr AI Manager

Honeywell Honeywell · Industrial · Bengaluru, Karnataka, India

This role is for a Sr AI Manager at Honeywell, focused on driving the end-to-end delivery of AI programs within the Value Engineering and Component Engineering Center of Excellence. The manager will translate strategy into execution plans, ensure rigorous delivery through program governance, and drive adoption and value realization. Key responsibilities include managing AI/ML/GenAI programs, MLOps/LLMOps capabilities, cloud platforms, governance, vendor partnerships, financial management, and building a high-performing AI team. The role requires strong leadership experience in AI/ML program delivery, understanding of AI/GenAI and MLOps/LLMOps, cloud platforms, governance, and people management.

What you'd actually do

  1. Lead the end-to-end delivery of AI programs, partnering with AI technical leadership and SMEs to translate strategy into executable plans and measurable business outcomes.
  2. Drive execution rigor across AI initiatives using strong program management practices (planning, governance, risk mitigation, stakeholder communication) to ensure predictable and high-quality delivery.
  3. Collaborate with vendors and cross-functional teams (IT, business, and functional groups) to enable seamless delivery, integration, and adoption of AI solutions across the organization.
  4. Own requirements evaluation, testing, adoption, and value realization for all AI tracks, ensuring solutions are effectively operationalized and deliver tangible impact.
  5. Establish and enforce robust documentation and governance standards, including model lineage, evaluation frameworks, compliance requirements, and audit-ready artefacts.

Skills

Required

  • 15+ years of overall experience with demonstrated leadership in AI/ML/Data analytics/program delivery, including planning, budgeting, stakeholder management, and team development
  • AI program delivery leadership: ability to run multi-workstream AI initiatives, manage dependencies, and deliver business outcomes with strong execution discipline.
  • AI/GenAI & MLOps/LLMOps literacy: solid working knowledge of AI/ML lifecycle, LLM/GenAI solution patterns, deployment/monitoring, and evaluation approaches.
  • Cloud & platform understanding (preferably GCP): familiarity with cloud-native architecture, scalable services, security controls, and operational readiness for production AI.
  • Governance, compliance & documentation: ability to enforce traceability (model lineage), evaluation frameworks, change control, and audit-ready documentation practices.
  • Vendor and cross-functional partnering: proven ability to align vendors, IT, and functional groups; negotiate deliverables/SLAs; and resolve delivery friction.
  • Process excellence & continuous improvement: benchmark practices, improve efficiency, strengthen quality gates, and institutionalize repeatable delivery mechanisms
  • Financial & operating management: resource planning, budgeting/AOP ownership, capacity modelling, and delivery trade-offs across build/run/vendor mix.
  • People leadership: hiring, coaching, performance management, succession planning, and building inclusive, accountable teams with strong engagement

What the JD emphasized

  • AI program delivery leadership
  • AI/GenAI & MLOps/LLMOps literacy
  • Governance, compliance & documentation
  • Vendor and cross-functional partnering
  • Financial & operating management
  • People leadership

Other signals

  • driving end-to-end delivery of AI programmes
  • translating strategy into structured execution plans
  • building a high-performing team
  • scaling talent
  • evolving tools and systems to support sustainable AI delivery