The Sr Software Engineering Manager is a senior technology leader responsible for shaping engineering strategy, driving cloud‑native architectures, delivering high‑quality software products, and incorporating modern AI/ML capabilities into the engineering ecosystem. This leader will champion intelligent automation, AI‑powered development practices, data‑driven product intelligence, and ML‑enabled customer experiences. You will lead multiple engineering teams building scalable, secure, and resilient systems on Azure, GCP, AWS while elevating developer productivity, platform maturity, and AI readiness across the organization. At Honeywell, our approach is simple. We hire talented people, nurture their growth, give them opportunities to make a difference, and promote from within.

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Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.

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As a Senior Software Engineering Manager at Honeywell, you will be responsible for leading the software engineering management team, providing technical and strategic leadership for successful project delivery, driving the adoption of new technologies, and identifying opportunities for process improvement to ensure the development of high-quality software solutions.

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· 12–18+ years of software engineering experience with 5+ years leading multiple engineering teams. · Proven experience delivering cloud‑native distributed systems on Azure. · Hands‑on leadership with AI/ML platforms or integrating ML models into production systems. · Track record of building engineering organizations through periods of architectural transformation. Preferred · Experience building AI‑enhanced SaaS or platform products. · Familiarity with Generative AI, LLMs, RAG architectures, vector databases. · Experience managing hybrid cloud or edge‑AI deployments. · Exposure to responsible AI frameworks and AI governance.· Strong background in cloud architecture, microservices, containers, and serverless patterns. · Practical knowledge of ML frameworks (TensorFlow, PyTorch, Scikit‑learn) and data pipelines. · Experience with MLOps tools (Azure ML, Databricks, MLflow, KubeFlow). · Proficiency in at least one modern programming language (Python, Go, Java, C#, TypeScript). · Solid grounding in security, observability, compliance, and scalable distributed system design. · Inspiring leader with the ability to scale high‑performing engineering teams. · Strong communicator able to influence at all levels including executives and cross‑functional partners. · Strategic thinker with strong decision‑making under ambiguity. · Customer‑centric mindset with enthusiasm for innovation and emerging technologies, especially AI/ML.

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Key Responsibilities

· Engineering & Technical Strategy: Drive the overall engineering vision and modernization roadmap, advance cloud‑native architectures, set standards for high‑quality and secure development, and embed AI/ML practices into engineering processes and product capabilities.

· AI/ML Integration Across Engineering: Collaborate with Data Science and Product to shape the AI strategy, operationalize ML models in production with full lifecycle support, promote AI‑assisted development practices, build robust MLOps pipelines, and strengthen AI‑driven testing and automation.

· Product Engineering & Delivery: Lead delivery of AI‑enabled and cloud‑scale features, build scalable API‑first platforms, ensure strong performance across key delivery KPIs, and use AI telemetry and analytics to enhance product decisions.

· People & Organizational Leadership: Develop and mentor engineering talent, evolve the organization toward AI‑ready and automation‑centric practices, foster continuous learning in modern engineering disciplines, and guide adoption of new AI tools and frameworks.

· Platform, Infrastructure, and MLOps: Advance platform engineering through automation and ML‑ready pipelines, extend reliability engineering to ML systems, support scalable cloud infrastructure for data and model workloads, and enforce governance for AI security and responsible use.

· Quality Engineering, Automation & Intelligent Validation: Strengthen shift‑left quality practices, use AI‑driven insights and anomaly detection to elevate quality, and define benchmarks that support both traditional software and ML lifecycle requirements.

· Executive Leadership & Stakeholder Management: Communicate technical and AI strategy to executives, partner across product and technology functions, and oversee budgets, cloud spend, vendor relationships, and investment planning for AI‑driven initiatives.