Description- 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.
ResponsibilitiesKey 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 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.
Qualifications- Bachelors or Masters 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. 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.
DescriptionFull-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 tech...
Description- 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.
ResponsibilitiesKey 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 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.
Qualifications- Bachelors or Masters 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. 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.
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