Honeywell

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.

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.

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.

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.