Description:

Honeywell is hiring an AI Engineer I at its RMZ Ecoworld tech campus in Bangalore to design, implement, and scale intelligence-driven modules within its automated software suites. Working closely with senior engineering architects, you will participate in full-lifecycle predictive modeling and continuous deployment frameworks to optimize industrial automation, aviation safety analytics, and energy transition platforms. This entry-level to early-career role centers on refining raw data patterns into deployable machine learning and deep learning algorithms, constructing evaluation protocols, and driving the integration of these AI capabilities directly into modern enterprise software like Honeywell Forge.

Key Technologies:

Python, TensorFlow, PyTorch, Machine Learning and Deep Learning algorithms, Agile methodologies

Requirements:

• Bachelor’s degree from an accredited institution in a core technical STEM discipline (Science, Technology, Engineering, or Mathematics). An Advanced degree (Master’s or Ph.D.) in Computer Science or Artificial Intelligence is highly valued.
• Strong conceptual and hands-on exposure running data science, machine learning, and deep learning engineering technology workflows.
• Deep programming proficiency using Python alongside major tensor execution libraries and deep learning frameworks (such as TensorFlow and PyTorch) to train neural networks.
• Practical capacity to assist in the mathematical design, hyperparameter tuning, testing, and continuous deployment of predictive models and algorithmic subsystems.
• Collaboration skills to assist cross-functional software teams in wrapping AI models into secure API abstractions and injecting them smoothly into existing system architectures.
• Familiarity working within modern fast-paced Agile sprint development environments, maintaining clear technical code version controls, and executing routine unit validations.
• Sharp attention to detail, strong problem-solving capacities, intellectual curiosity to stay current on foundational model updates, and an adaptive collaborative approach.