Honeywell

Develops and implements AI algorithms, models, and systems for industrial applications, collaborating with cross-functional teams to integrate solutions and ensure scalability and robustness.

Lead Software Architect for AI/ML & Cloud Platforms at Honeywell, focusing on hands-on software development, AI/ML systems, and scalable architecture. The role involves defining technical direction, designing and prototyping solutions, and collaborating with data scientists and engineers on ML pipelines and production-grade AI/ML systems. Key responsibilities include software architecture, AI/ML engineering, cloud platform design, and technical leadership.

Lead hands-on development and deployment of Edge AI solutions, focusing on model optimization and MLOps pipelines for embedded platforms with GPUs/AI accelerators.

Senior Software Engineer focused on building and deploying scalable backend services and APIs, with a strong emphasis on MLOps pipelines, observability, and CI/CD within an industrial and safety-critical aerospace context.

Lead Software Architect role at Honeywell focusing on designing and developing scalable software architectures for process and DCS solutions, incorporating AI/ML and Azure. The role involves researching emerging technologies, architecting microservices, and ensuring system integration and scalability within the industrial domain.

Advanced AI Engineer at Honeywell, responsible for leading the design and development of AI algorithms, models, and systems, acting as a subject matter expert, and driving technical excellence in AI initiatives. The role involves end-to-end AI development lifecycle management, collaboration with cross-functional teams, and mentorship of AI engineers.

Develops and implements AI algorithms, models, and systems for industrial applications, collaborating with cross-functional teams to integrate solutions and ensure scalability and robustness.

Seeking an ML Engineer to design, implement, and operate scalable ML platforms on Databricks, focusing on reliable model development, deployment, monitoring, and lifecycle management for large-scale AI workloads. Responsibilities include end-to-end ML lifecycle management, automation frameworks, governed experiment tracking, model serving, monitoring, feature management, and optimization.

Advanced Software Engineer at Honeywell in Bengaluru, India, focusing on Data Engineering and ML Operations for production-grade AI solutions. The role involves leading the development of AI systems using various learning techniques, optimizing model performance, and integrating third-party AI services. Requires strong Python, ML libraries, MLOps tools, cloud platforms, and big data experience, with exposure to LLMs, vector databases, and edge AI.

Manager for an industrial AI/ML engineering team, focusing on building and deploying intelligent solutions for predictive maintenance, process optimization, and smart automation. Requires strong leadership, AI/ML strategy, production deployment experience, and industrial systems integration.

Advanced AI Engineer at Honeywell, responsible for leading the design and development of AI algorithms, models, and systems, acting as a subject matter expert, and driving technical excellence in AI initiatives. The role involves end-to-end AI development lifecycle management, collaboration with cross-functional teams, and mentorship of AI engineers.

This role is for a Sr. Software Engineering Manager at Honeywell, focusing on leading engineering teams to build and scale reliable, secure, and resilient cloud-native systems on Azure, GCP, and AWS. The manager will be responsible for driving engineering strategy, integrating AI/ML practices into engineering processes and product capabilities, operationalizing ML models, building MLOps pipelines, and leading the delivery of AI-enabled features. The role also involves people leadership, advancing platform engineering, strengthening quality engineering with AI-driven insights, and managing budgets for AI-driven initiatives. Experience with SRE, Data and AI platforms, and integrating ML models into production systems is required.

Senior Director of Software Engineering leading AI/ML and Ontology platforms, focusing on architecture, strategy, and execution of scalable, explainable AI-enabled solutions in an enterprise context. Requires deep expertise in AI/ML, ontology design, and leading senior technical teams.

This role focuses on advanced manufacturing engineering, optimizing processes, and implementing technologies like automation and robotics. While it mentions using AI/ML for specific tasks like quality control and logistics, the core of the role is in traditional manufacturing engineering, not AI model development.

This role focuses on architecting and overseeing the AI model lifecycle from experimentation to production deployment, including monitoring performance, drift, and latency. It also involves driving security by design, DevSecOps practices, and cloud-native architectures for complex software systems.

Lead SRE role focused on reliability, scalability, and performance of production systems, including AI-enabled services. Responsibilities include defining SRE standards, automation, cloud infrastructure management, operationalizing ML workloads, incident management, and mentorship. Requires expertise in cloud, observability, CI/CD, and familiarity with ML pipelines and MLOps tools.

This role is for an Advanced Software Engineer at Honeywell, focusing on R&D for industrial automation systems. The engineer will research, design, and develop software using advanced technologies, including AI, and contribute to innovations for customers worldwide. The role requires experience in process control, advanced process control, and domain expertise in industrial sectors. The engineer will also provide customer support and participate in technical strategic planning.

Senior Advanced Software Architect at Honeywell focusing on cybersecurity architecture, threat strategy, and technical direction. The role involves leading the design and review of secure software architectures, researching emerging technologies like AI/ML, analyzing cybersecurity threats, and ensuring security best practices are integrated into systems. Requires strong software development experience in cybersecurity and a solid understanding of cloud and microservice architectures. Experience coding AI/ML pipelines is a plus.