• 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.

• Bachelor’s or Master’s degree in Computer Science, AI, or related technical field.

• 5+ years of hands-on experience developing and deploying ML models in production.

• Proven track record in advanced machine learning frameworks (e.g., Tensor Flow, Py Torch).

• 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.

Key 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 Time Series, 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.