Advanced AI Engr

As a Advanced AI Engineer at Honeywell, you will be a driving force in designing, developing, and deploying end-to-end AI/ML solutions aimed at bringing autonomous capabilities to Honeywell products over the next decade. You will operate as a hands-on technical leader, working on everything from data pipelines to model optimization and drift detection, while mentoring junior team members to build a truly full-stack AI/ML practice.

• 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.

• Exposure towards Data Engineering – Data pipeline creation , Cleansing , interpolation , Data Mesh , Data lite , Know the Spark /Databricks , Basic knowledge in Scala , Python.

• Mentor junior engineers, promoting best practices in model development and deployment.

• 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.

Qualifications & Experience

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

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

• Proven track record in advanced machine learning frameworks (e.g., TensorFlow, PyTorch).

• 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.