Advanced Software Engr

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 what's 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.