Machine Learning Engineer - IV (Computer Vision)
J
JobgetherComputer Vision
IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of professional experience in machine learning engineering, including at least 3 years focused on biometrics or face analysis.
- Required Skills
- AWSPythonMachine LearningOpenCVPyTorchAirflowTensorflowMLOpsComputer Vision
Requirements
- 5+ years of professional experience in machine learning engineering, including at least 3 years focused on biometrics or face analysis.
- Deep knowledge of computer vision techniques, especially face recognition systems and image-based machine learning applications.
- Strong understanding of algorithmic fairness principles and practical experience identifying and mitigating bias in AI models.
- Advanced Python programming skills with experience using machine learning and computer vision libraries such as OpenCV, Pillow, and PyTorch.
- Proven experience designing complete ML pipelines from data preparation and model training through deployment and production operations.
- Hands-on experience scaling training workloads using multi-GPU environments and deploying ML solutions on AWS services such as SageMaker, EC2, or EKS.
- Experience with workflow orchestration tools such as Airflow and modern ML engineering practices.
- Strong software engineering skills with the ability to write clean, maintainable, and production-ready code.
- Excellent communication skills and the ability to collaborate with technical teams and stakeholders.
Responsibilities
- Lead the architecture, development, and optimization of computer vision solutions focused on biometric applications, including face detection, recognition, quality assessment, and attribute analysis.
- Build, train, evaluate, and improve machine learning models using frameworks such as PyTorch, TensorFlow, and/or JAX.
- Own end-to-end machine learning pipelines, including data ingestion, preparation, training workflows, deployment, and monitoring.
- Design automated data workflows using tools such as Airflow, ensuring high-quality, diverse, and balanced datasets for model development.
- Conduct fairness analysis and benchmarking of biometric models across different datasets and operating conditions to reduce bias and improve reliability.
- Optimize ML models for production environments through techniques such as quantization, distillation, TensorRT, and ONNX.
- Deploy and scale machine learning services using cloud infrastructure, including AWS-based environments.
- Mentor other machine learning engineers, contribute to technical reviews, and promote engineering best practices.
- Collaborate with cross-functional teams to deliver secure, scalable, and high-performing AI solutions.
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