AI/ML Engineer

Job Summary:

We are seeking a highly skilled and motivated AI/ML Engineer with a specialization in Computer Vision & Un-Supervised Learning to join our growing team. You will be responsible for building, optimizing, and deploying advanced video analytics solutions for smart surveillance applications, including real-time detection, facial recognition, and activity analysis.

This role combines the core competencies of AI/ML modelling with the practical skills required to deploy and scale models in real-world production environments, both in the cloud and on edge devices.

Key Responsibilities:

AI/ML Development & Computer Vision

Design, train, and evaluate models for:

o Face detection and recognition

o Object/person detection and tracking

o Intrusion and anomaly detection

o Human activity or pose recognition/estimation

Work with models such as YOLOv8, DeepSORT, RetinaNet, Faster-RCNN, and InsightFace.

Perform data preprocessing, augmentation, and annotation using tools like LabelImg, CVAT, or custom pipelines.

?? Surveillance System Integration

Integrate computer vision models with live CCTV/RTSP streams for real-time analytics.

Develop components for motion detection, zone-based event alerts, person re-identification, and multi-camera coordination.

Optimize solutions for low-latency inference on edge devices (Jetson Nano, Xavier, Intel Movidius, Coral TPU).

Model Optimization & Deployment

Convert and optimize trained models using ONNX, TensorRT, or OpenVINO for real-time inference.

Build and deploy APIs using FastAPI, Flask, or TorchServe.

Package applications using Docker and orchestrate deployments with Kubernetes.

Automate model deployment workflows using CI/CD pipelines (GitHub Actions, Jenkins).

Monitor model performance in production using Prometheus, Grafana, and log management tools.

Manage model versioning, rollback strategies, and experiment tracking using MLflow or DVC.

As an AI/ML Engineer, you should be well-versed of AI agent development and finetuning experience

?? Collaboration & Documentation

Work closely with backend developers, hardware engineers, and DevOps teams.

Maintain clear documentation of ML pipelines, training results, and deployment practices.

Stay current with emerging research and innovations in AI vision and MLOps.

Required Qualifications:

Bachelors or masters degree in computer science, Artificial Intelligence, Data Science, or a

related field.

3 - 6 years of experience in AI/ML, with a strong portfolio in computer vision, Machine Learning.

Hands-on experience with:

o Deep learning frameworks: PyTorch, TensorFlow

o Image/video processing: OpenCV, NumPy

o Detection and tracking frameworks: YOLOv8, DeepSORT, RetinaNet.

Solid understanding of deep learning architectures (CNNs, Transformers, Siamese Networks).

Proven experience with real-time model deployment on cloud or edge environments.

Strong Python programming skills and familiarity with Git, REST APIs, and DevOps tools.

Preferred Qualifications:

Experience with multi-camera synchronization and NVR/DVR systems.

Familiarity with ONVIF protocols and camera SDKs.

Experience deploying AI models on Jetson Nano/Xavier, Intel NCS2, or Coral Edge TPU.

Background in face recognition systems (e.g., InsightFace, FaceNet, Dlib).

Understanding of security protocols and compliance in surveillance systems.

Tools & Technologies:

Category Tools & Frameworks

Languages & AI Python, PyTorch, TensorFlow, OpenCV, NumPy, Scikit-learn

Model Serving FastAPI, Flask, TorchServe, TensorFlow Serving, REST/gRPC APIs

Model Optimization ONNX, TensorRT, OpenVINO, Pruning, Quantization

Deployment Docker, Kubernetes, Gunicorn, MLflow, DVC

CI/CD & DevOps GitHub Actions, Jenkins, GitLab CI

Cloud & Edge AWS SageMaker, Azure ML, GCP AI Platform, Jetson, Movidius, Coral TPU

Monitoring Prometheus, Grafana, ELK Stack, Sentry

Annotation Tools LabelImg, CVAT, Supervisely