At Honeywell, we are a leading technology company that is shaping a smarter, safer, and more sustainable world. We create and manufacture technologies that address some of the world’s most critical challenges around energy, safety, security, productivity, and global urbanization. As a Advanced Software Engineer on our team, you will be at the heart of this innovation, developing next-generation solutions that blend cutting-edge software engineering with the power of Artificial Intelligence and Machine Learning.

Join a team that is dedicated to pushing the boundaries of whats possible.

Position Summary:

We are seeking a talented and driven Software Engineer II with a strong background in Python and a passion for Artificial Intelligence and Machine Learning. In this role, you will be responsible for designing, developing, and deploying robust software solutions that leverage AI/ML models to solve complex problems across Honeywell’s business units, including Aerospace, Building Technologies, and Safety and Productivity Solutions.

You will work as part of an agile team to build end-to-end ML-powered applications, from data ingestion and model training to deployment and performance monitoring in a production environment.

Key Responsibilities:

Develop & Implement Solutions: Design, code, test, and deploy high-quality software solutions using Python and its ecosystem.

Build ML Pipelines: Create and maintain robust, scalable data and machine learning pipelines for training, evaluation, and inference.

Integrate AI/ML Models: Collaborate with Data Scientists to integrate, productionize, and scale machine learning models (e.g., predictive maintenance, anomaly detection, computer vision, NLP).

Data Engineering: Work with large, complex datasets. Perform data preprocessing, feature engineering, and data validation to prepare data for machine learning applications.

Contribute to Architecture: Participate in technical design and architecture discussions, helping to shape the future of our AI-driven products.

Embrace MLOps: Utilize modern MLOps practices and tools for model versioning, continuous integration/continuous deployment (CI/CD), and operational monitoring.

Collaborate Cross-Functionally: Work closely with product managers, data scientists, and other engineering teams to translate business requirements into technical solutions.

Ensure Quality: Write clean, maintainable, and well-documented code. Develop unit tests and integration tests to ensure software reliability and robustness.

You Must Have (Basic Qualifications):

Bachelor’s degree in computer science, Engineering, Data Science, or a related technical field.

2+ years of professional software development experience.

Strong proficiency in Python and experience with its scientific computing and data manipulation libraries (e.g., NumPy, Pandas, Scikit-learn).

Hands-on experience developing and implementing machine learning models using frameworks like TensorFlow, PyTorch, or Keras.

Solid understanding of core machine learning concepts (e.g., regression, classification, clustering, deep learning).

We Value (Preferred Qualifications):

Master’s or Ph.D. in a relevant field.

Experience with cloud platforms (Azure, AWS, or GCP) and their associated AI/ML services (e.g., Azure ML, AWS SageMaker).

Familiarity with MLOps tools and principles, including containerization with Docker, orchestration with Kubernetes, and CI/CD pipelines (e.g., Jenkins, Azure DevOps).

Experience in a specific AI/ML domain such as Computer Vision (OpenCV), Natural Language Processing (NLTK, spaCy, Hugging Face), or Time-Series Analysis.

Knowledge of big data technologies like Apache Spark or Hadoop.

Experience building and consuming RESTful APIs using frameworks like Flask or Django.

Proficiency with version control systems, particularly Git.

Experience working in an Agile/Scrum development environment.

Excellent problem-solving skills and the ability to work independently as well as in a team.

Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders.