Software Engr II

We are seeking a Video Analytics Engineer with 3-5 years of hands-on experience to build, improve, and optimize computer vision and video processing solutions that convert raw video streams into actionable intelligence. This role is ideal for an engineer who is strong in implementation and deployment, while also being curious about model behavior, failure analysis, and practical experimentation to improve accuracy in real-world scenarios.

You will work at the intersection of deep learning, real-time video processing, and scalable backend systems, contributing to use cases across security, retail, traffic, and industrial environments. You will partner with senior engineers and research-oriented team members to adapt existing approaches, evaluate results, and help move promising ideas into reliable production systems.

Your role will also include overseeing, supervising and reviewing tasks performed by team members to ensure effective execution of work; managing end‑to‑end processes and projects for both internal and external clients with responsibility for timely and accurate delivery; issuing clear instructions and directions to team members on tasks to be performed; and mentoring and guiding junior colleagues to support their skill development, professional growth, and overall success

Responsibilities

• Design and develop end-to-end video analytics pipelines covering ingestion, decoding, preprocessing, inference, and post-processing.
• Build, fine-tune, test, and deploy computer vision models for object detection, tracking, classification, action recognition, and anomaly detection.
• Analyze model outputs, identify common failure cases, and support experiments to improve robustness under occlusion, crowd density, lighting variation, and camera diversity.
• Optimize inference performance on edge and cloud GPUs using TensorRT, ONNX Runtime, OpenVINO, or similar runtimes.
• Implement real-time video processing using GStreamer, FFmpeg, or DeepStream, with low-latency streaming over RTSP, WebRTC, or HLS.
• Support dataset curation, annotation review, data augmentation, and retraining workflows to improve model quality over time.
• Collaborate with product, QA, and senior technical teams to translate business requirements into deployable analytics features.
• Contribute to benchmarking, validation, and performance measurement using clear metrics for accuracy, latency, and throughput.
• Engineer backend services and supporting utilities for multi-camera orchestration, metadata handling, and monitoring.
• Maintain documentation, write unit and integration tests, and follow CI/CD best practices for model and service releases.

Qualifications

• B.E./B.Tech/M.E./M.Tech in Computer Science, Electronics, AI/ML, Mathematics, or a related field.
• 3-5 years of professional experience in computer vision, video analytics, machine learning, or a related engineering role.
• Strong programming skills in Python and preferably C++ along with good command of Linux development environments.
• Hands-on experience with deep learning frameworks such as PyTorch and/or TensorFlow.
• Practical knowledge of video processing and CV tooling such as OpenCV, GStreamer, FFmpeg, or NVIDIA DeepStream.
• Experience deploying or optimizing models using TensorRT, ONNX, OpenVINO, or similar inference frameworks.
• Good understanding of object detection, tracking, and classification techniques, with the ability to evaluate model behavior using real-world data.
• Working knowledge of performance profiling across CPU/GPU pipelines and an ability to troubleshoot latency or throughput bottlenecks.
• Exposure to REST/gRPC services, message queues, and containerized deployments using Docker.
• Strong problem-solving ability, structured debugging skills, and comfort working in cross-functional product environments.

Preferred Qualifications

• Experience with NVIDIA Jetson, edge GPUs, NVRs, or other resource-constrained deployment platforms.
• Exposure to action recognition, pose estimation, segmentation, face recognition, or license plate recognition.
• Familiarity with model benchmarking, experiment tracking, and validation workflows.
• Understanding of challenging deployment conditions such as crowding, occlusions, low light, and varied camera angles.
• Basic exposure to MLOps tooling such as MLflow, DVC, Weights & Biases, or Kubeflow.
• Awareness of streaming protocols such as RTSP, WebRTC, HLS, or SRT.
• Interest in applied research, experimentation, and adapting recent computer vision approaches for production use.

About Us

Honeywell Technologies is a global, pure-play automation company with a legacy of innovating to help solve the world’s most mission-critical challenges, enhancing the quality of life for people and communities around the world. We serve the building, industrial and process sectors with a broad portfolio of services, solutions and products, underpinned by our Honeywell Technologies Accelerator operating system and Honeywell Technologies Forge intelligence layer. By combining the deep domain expertise of our more than 50,000 employees with decades of data from our global installed base, we are uniquely positioned to lead the industrial sector’s transition from automation to autonomy.