Senior AI Manager
Honeywell · Dublin
Senior AI Engineering Manager
People Leadership | Technical Execution | Production AI Engineering
| A hands-on engineering leadership role. The Senior AI Engineering Manager owns team outcomes, engineering execution, and the delivery of secure, scalable AI capabilities from experimentation through sustained production operation. |
As Senior AI Engineering Manager for the Honeywell Forge and AI organization, you will lead a global AI engineering capability responsible for designing, building, integrating, and operating enterprise-grade AI capabilities for Honeywell products. You will own not only team execution, but also portfolio direction, talent strategy, customer-facing technical leadership, and the organizational mechanisms required to scale AI delivery.
You will combine people leadership with strong engineering judgment and hands-on technical engagement. You will shape the AI engineering roadmap, convert product and business priorities into executable plans, guide architecture and implementation, and ensure that solutions meet expectations for reliability, scalability, security, responsible AI, compliance, and cost.
You will report to the Director of Artificial Intelligence and work from our Ireland location on a 3:2 hybrid schedule, with three days per week onsite and two days remote. Your contributions will help scale AI engineering across Honeywell products, strategic customer engagements, and global delivery teams.
Key responsibilities AI engineering strategy and execution- Partner with product managers, technical product owners, architects, and business leaders to translate customer and product requirements into AI engineering roadmaps, architectures, milestones, and delivery plans.
- Lead the end-to-end engineering of AI capabilities, from rapid experimentation and technical validation through integration, production release, operation, and continuous improvement.
- Remain technically engaged through architecture reviews, design decisions, code and implementation reviews, prototyping, troubleshooting, and resolution of critical engineering risks.
- Drive engineering for full-stack AI solutions spanning data and feature pipelines, model and LLM integration, APIs, microservices, orchestration, user-facing workflows, and cloud, on-premises, or edge deployment.
- Balance delivery speed with software quality, platform reuse, maintainability, performance, security, and total cost of ownership.
- Establish and enforce engineering standards for source control, code review, automated testing, documentation, CI/CD/CT, reproducibility, release management, and safe rollback.
- Guide teams in production patterns for model and LLM serving, evaluation, observability, performance and drift monitoring, cost tracking, retraining, and operational support.
- Collaborate with Data Engineering, Platform Engineering, SRE, IT, Security, and Compliance to create reliable paths from prototypes to governed production services.
- Ensure appropriate controls for responsible AI, privacy, data governance, model lineage, security, compliance, and risk management.
- Promote reusable platforms, reference architectures, templates, and inner-source assets that accelerate delivery across multiple Honeywell product teams.
- Lead, coach, and develop a high-performing team of AI engineers, including goal setting, regular feedback, performance management, career development, succession planning, and recognition.
- Plan team capacity and skills against portfolio priorities; recruit, onboard, and retain engineering talent as the organization grows.
- Create an accountable engineering culture centred on customer outcomes, technical excellence, ownership, curiosity, respectful challenge, experimentation, and continuous learning.
- Develop technical leaders within the team and raise organizational capability through mentoring, design reviews, playbooks, demonstrations, training, and communities of practice.
- Coordinate work across teams, surface dependencies and delivery risks early, and provide clear status, options, and recommendations to senior stakeholders.
- Evaluate emerging AI technologies and guide pragmatic adoption across machine learning, time-series, computer vision, NLP, Generative AI, RAG, fine-tuning, and Agentic AI.
- Communicate architecture, delivery status, technical trade-offs, operational risks, and business impact clearly to engineers, product teams, senior executives, customers, and partners.
- Champion the value of production AI internally and externally, representing Honeywell in customer briefings, technical forums, conferences, industry panels, and partner discussions where appropriate.
- Own and govern the AI engineering portfolio, balancing investment across customer commitments, product innovation, reusable platforms, and technical debt to maximize business impact and speed-to-value.
- Lead annual and quarterly operating planning for the function, including roadmap definition, budgeting, workforce and capacity planning, hiring priorities, delivery governance, and investment trade-offs.
- Apply outcome- and evidence-based prioritization across the opportunity pipeline, ensuring AI investments are tied to measurable customer value, operational improvement, product differentiation, and commercial potential.
- Lead customer discovery engagements, technical workshops, executive briefings, and demonstrations that translate complex industrial challenges into credible AI engineering roadmaps and delivery plans.
- Represent Honeywell AI Engineering in strategic customer engagements, global events, industry forums, and partner discussions, using market feedback to influence product strategy and technology roadmaps.
- Build trusted relationships with business, product, and customer stakeholders to validate emerging AI concepts and accelerate adoption of scalable, production-ready capabilities.
- Build and scale geographically distributed AI engineering teams, establishing repeatable practices for recruitment, onboarding, technical development, mobility, and retention.
- Develop sustainable talent pipelines through university partnerships, graduate and internship programs, external research relationships, and targeted hiring initiatives.
- Create engineering enablement mechanisms such as MLOps and LLMOps labs, playbooks, architecture reviews, communities of practice, and mentoring programs that improve consistency and reduce time to production impact.
- Foster invention and intellectual-property creation by coaching teams to identify differentiated technical ideas, capture invention disclosures, and transfer research outcomes into product engineering backlogs.
- Bachelor's degree from an accredited institution in Computer Science, Software Engineering, Electrical Engineering, AI/ML, or a related technical discipline.
- 10+ years of relevant experience across software engineering, machine learning engineering, AI engineering, data engineering, or related product-development roles.
- 4+ years of experience leading and developing technical teams, including direct people-management responsibility.
- 5+ years of hands-on software development experience with Python and production engineering practices, including APIs, automated testing, version control, CI/CD, and design reviews.
- Proven delivery and operation of enterprise-grade AI/ML systems, with experience moving solutions from experimentation into reliable production use.
- Hands-on experience with modern AI/ML frameworks and data technologies such as PyTorch, TensorFlow, scikit-learn, Spark, MLflow, Hugging Face, or equivalent.
- Experience with cloud-native engineering and deployment technologies such as Azure, GCP, or AWS; Docker; Kubernetes; and managed data or AI platforms.
- Experience delivering Generative AI solutions, including RAG, LLM evaluation, model or API integration, orchestration, and Agentic AI patterns.
- Strong knowledge of MLOps and LLMOps, including model lifecycle management, evaluation, production monitoring, reproducibility, governance, and retraining strategies.
- Demonstrated ability to communicate complex engineering topics and trade-offs to technical teams, product leaders, customers, and executive stakeholders.
- Master's degree in Computer Science, Software Engineering, AI/ML, or a related field.
- Experience building AI capabilities for multi-tenant SaaS products or other mission-critical enterprise software.
- Experience deploying and optimizing AI workloads across cloud, on-premises, and edge environments.
- Depth in one or more relevant domains such as industrial AI, autonomous systems, time-series forecasting and optimization, computer vision, or intelligent agents.
- Experience creating shared AI platforms, reusable services, internal developer tooling, reference architectures, or engineering standards adopted across teams.
- A record of developing diverse technical talent and building inclusive, high-performing engineering teams.
- External technical presence through customer engagement, conference participation, publications, patents, standards work, or open-source contribution.