The Role
Design, build, and operate end-to-end ML pipelines for computer vision and generative AI. Own data ingestion, training, distributed scaling, deployment (cloud, on-prem, edge), CI/CD, monitoring, and model lifecycle management. Mentor engineers and collaborate with cross-functional teams to integrate emerging AI/ML technologies and optimize production inference and retraining workflows.
Summary Generated by Built In
We are seeking a highly skilled Senior Advanced Software Engineer with deep expertise in computer vision, deep learning, and generative AI (GenAI). This role requires end-to-end ownership of machine learning pipelines, from research and prototyping to deployment and scaling in production environments. The ideal candidate will have profound knowledge of full-stack ML deployment and a proven track record of building robust, scalable systems
ResponsibilitiesKey Responsibilities
- Architect and develop end-to-end ML pipelines for computer vision, deep learning, and GenAI use cases.
- Design scalable solutions for data ingestion, preprocessing, training, evaluation, and deployment.
- Implement and maintain full-stack ML deployment frameworks across cloud and on-prem environments.
- Automate CI/CD workflows for ML models to ensure reproducibility and reliability.
- Monitor, troubleshoot, and optimize deployed models for performance, accuracy, and efficiency.
- Collaborate with cross-functional teams including data scientists, product managers, and DevOps engineers.
- Mentor junior engineers and contribute to technical knowledge sharing across the organization.
- Drive innovation by integrating emerging AI/ML technologies into production workflows.
- Optimize ML systems for large-scale data, distributed training, and high-throughput inference.
- Establish best practices for model versioning, monitoring, retraining, and lifecycle management
Required Qualifications
- Strong background in computer vision, deep learning, and GenAI frameworks (e.g., PyTorch, TensorFlow, Hugging Face).
- Expertise in end-to-end ML pipeline development (data engineering, model training, deployment, monitoring).
- Expertise in deploying ML models to Edge ( x86, arm64 architectures )
- Proficiency in cloud platforms (AWS, Azure, GCP) and containerization/orchestration (Docker, Kubernetes).
- Solid understanding of MLOps practices (CI/CD, model versioning, monitoring, retraining).
- Experience with full-stack ML deployment including APIs, microservices, and scalable inference systems.
- Strong programming skills in Python, C++/Java, and modern ML toolchains.
- Excellent problem-solving, communication, and leadership skills.
Preferred Qualifications
- Experience with distributed training frameworks (Horovod, DeepSpeed).
- Knowledge of edge deployment for computer vision models.
- Familiarity with data pipelines (Spark, Kafka, Airflow).
- Contributions to open-source ML/AI projects.
- B.Tech/ M.Tech with 8+ years of experience
Skills Required
- Strong background in computer vision, deep learning, and generative AI frameworks (PyTorch, TensorFlow, Hugging Face)
- Expertise in end-to-end ML pipeline development including data engineering, model training, deployment, and monitoring
- Expertise in deploying ML models to edge architectures (x86, arm64)
- Proficiency with cloud platforms (AWS, Azure, GCP) and containerization/orchestration (Docker, Kubernetes)
- Solid understanding of MLOps practices (CI/CD, model versioning, monitoring, retraining)
- Experience with full-stack ML deployment including APIs, microservices, and scalable inference systems
- Strong programming skills in Python, C++, and Java
- Excellent problem-solving, communication, and leadership skills
- Experience with distributed training frameworks (Horovod, DeepSpeed)
- Familiarity with data pipeline technologies (Spark, Kafka, Airflow)
- Contributions to open-source ML/AI projects
- B.Tech/M.Tech with 8+ years of experience
Honeywell Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Honeywell and has not been reviewed or approved by Honeywell.
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Retirement Support — Retirement plans feature a notably strong company 401(k) match with vesting after three years, enhancing long-term savings security. Additional tax-advantaged accounts and company contributions for eligible earners further strengthen financial preparedness.
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Leave & Time Off Breadth — Time off policies include flexible or unlimited vacation for many salaried roles and a broad observed-holiday schedule, providing manager-approved flexibility. This structure supports rest and work-life balance across varied needs.
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Parental & Family Support — Parental leave offers paid time for birth, adoption, or foster care that can be taken consecutively or intermittently. The design enables practical flexibility in how family leave is used.
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The Company
What We Do
Honeywell is a Fortune 500 company that invents and manufactures technologies to address tough challenges linked to global macrotrends such as safety, security, and energy. With approximately 110,000 employees worldwide, including more than 19,000 engineers and scientists, we have an unrelenting focus on quality, delivery, value, and technology in everything we make and do.
