About the Role :

We are looking for a Machine Learning Engineer with strong expertise in Computer Vision and Biometrics to design, build, and scale production-grade face recognition systems. The ideal candidate should have hands-on experience developing deep learning models, optimizing them for real-time inference, and deploying end-to-end ML pipelines on cloud infrastructure.

Key Responsibilities :

- Design and develop computer vision models for face detection, face recognition, face quality assessment, and facial attribute analysis.

- Train, fine-tune, and optimize deep learning models using PyTorch, TensorFlow, and/or JAX.

- Build and maintain end-to-end ML pipelines covering data ingestion, preprocessing, training, deployment, and monitoring.

- Design automated data pipelines using Apache Airflow.

- Curate balanced datasets and leverage synthetic data generation techniques to improve model quality and diversity.

- Optimize models for production inference using ONNX, TensorRT, quantization, and model distillation.

- Deploy and manage ML workloads on AWS (SageMaker, EC2, EKS).

- Perform benchmarking, fairness analysis, and performance evaluation across diverse datasets.

- Collaborate with engineering and product teams to build scalable, low-latency biometric solutions.

- Mentor engineers and participate in architecture and design reviews.

Required Skills :

- 5+ years of industry experience in Machine Learning.

- Minimum 3 years of hands-on experience in Biometrics, Face Recognition, Face Verification, or Face Analysis.

- Strong expertise in Computer Vision and Deep Learning.

- Hands-on experience with PyTorch, TensorFlow, or JAX.

- Strong programming skills in Python.

- Experience with OpenCV and related computer vision libraries.

- Experience building production-grade ML pipelines.

- Hands-on experience with AWS (SageMaker, EC2, EKS).

- Experience with Kubernetes, Docker, and cloud-native deployments.

- Experience optimizing models for low-latency inference.