Design and implement high-impact AI/ML models and workflows, 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.
Key Skills and Qualifications
Bachelor’s or Master’s degree in Computer Science, AI, or a related technical field, with knowledge of Agile or similar software development methodologies; strong Python skills complemented by exposure to additional programming languages like Scala or Java.
Full-stack AI/ML experience encompassing all stages from data ingestion through model deployment and maintenance, supported by strong hands-on experience in developing and deploying ML models in production.
Strong analytical mindset focused on skeptical, data-driven decision-making, with a proven track record in advanced machine learning frameworks such as TensorFlow and PyTorch.
Familiarity with cloud platforms like AWS, Azure, or GCP for large-scale training and deployment of models, alongside demonstrated expertise in MLOps tools and best practices, including CI/CD, containerization, and orchestration.
Ability to effectively communicate technical concepts to both technical experts and laypersons, facilitating collaboration across diverse teams.
Our Offer
A culture that fosters inclusion, diversity, and innovation in an international work environment
Market specific training and ongoing personal development.
Experienced leaders to support your professional development