The Role
Design, develop, and deploy production AI/ML and cloud solutions enabling autonomous Honeywell products. Build scalable data pipelines and MLOps workflows using Databricks, Vertex AI, MLflow, and Kubeflow. Develop solutions spanning time series, computer vision, NLP, generative AI, RAG, and agentic AI. Evaluate models for performance, bias, and drift, maintain code quality, collaborate across engineering teams, and mentor junior engineers.
Summary Generated by Built In
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.
ResponsibilitiesKey Responsibilities- 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.
- 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.
Skills Required
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related technical field
- 6+ years of hands-on experience developing and deploying machine learning models in production
- Proven experience with advanced machine learning frameworks such as TensorFlow or PyTorch
- Expertise in MLOps tools and best practices, including CI/CD, containerization, and orchestration
- Strong Python programming skills
- Full-stack AI/ML experience from data ingestion through model deployment and maintenance
- Familiarity with AWS, Azure, or GCP for large-scale training and deployment
- Ability to communicate technical concepts to technical and nontechnical audiences
- Knowledge of Agile or similar software development methodologies
- Exposure to Scala or Java
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
