Sr Advanced Software Engineer

As a Senior Advanced AI Engineer at Honeywell, you will be a driving force in designing, developing, and deploying end-to-end cloud and AI/ML solutions aimed at bringing autonomous capabilities to Honeywell products over the next decade. You will operate as a hands-on technical leader, working on everything from data pipelines to model optimization and drift detection, while mentoring junior team members to build a truly full-stack AI/ML practice.

• Bachelor s or Master s degree in Computer Science, AI, or related technical field.
• 6+ years of hands-on experience developing and deploying ML models in production.
• Proven track record in advanced machine learning frameworks (e. g. , TensorFlow, PyTorch).
• Demonstrated expertise in MLOps tools and best practices (CI/CD, containerization, orchestration).
• Strong Python skills, with exposure to additional languages (Scala, Java), considered a plus.

• Full-stack AI/ML experience (data ingestion through model deployment and maintenance).
• Strong analytical mindset with a bias towards skeptical, data-driven decision-making.
• Familiarity with cloud platforms (AWS, Azure, or GCP) for large-scale training and deployment.
• Ability to communicate technical concepts to both experts and laypersons.
• Knowledge of Agile or similar software development methodologies.

• Design and implement high-impact AI/ML models and workflows, ability to work on Cloud architectures and build solutions, ensuring scalability and reliability on cloud platforms such as Databricks, VertexAI, etc.
• Collaborate with cross-functional teams (Data Engineering, ML Engineering, DevOps) to create holistic MLOps pipelines, leveraging frameworks such as MLflow and Kubeflow.
• Conduct thorough reviews of ML models for performance, bias, and drift, proposing corrective actions.
• Integrate AI (including TimeSeries, Computer Vision, NLP, GenAI/RAG/Agentic AI) solutions into existing Honeywell products, maintaining rigorous code quality standards.
• Mentor junior engineers, promoting best practices in model development and deployment.