- Face Recognition / ID Validation AI Engineer
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Descripción
Face Recognition / ID Validation AI Engineer
About the job
We are looking for an AI engineer with strong software development and machine learning skills. You will be working side by side with other machine learning and computer vision engineers, developing cutting-edge fraud detection technology in the fields of security and document intelligence, facial recognition and advanced anti-spoofing . This role would require you to do the following on a daily basis:
Formulate innovative approaches to combat fraud using computer vision and machine learning.
Create new features, train new models, deploy them into production environment.
Contribute by extending and improving our ML frameworks and platform, creating next-generation capabilities.
Build and deploy solutions to interesting computer vision or machine learning problems including document data extraction, fraud detection or biometric verification challenges.
Design and implement efficient pre-processing steps around digital images or video files.
Work alongside other machine learning and computer vision specialists in order to deliver on both short term objectives and long term goals.
Support and guide other engineers in learning about, applying and delivering product features driven by machine learning techniques.
About you
The ideal person for this role:
You have demonstrable experience in Face Recognition, Presentation Attack Detection or ID Validation.
Master's degree or PhD preferably in Computer Science, or equivalent experience.
You're enthusiastic and technically curious about ML and AI technologies.
Experienced in developing, deploying and operating ML models in production.
Hands on experience working on computer vision and machine learning projects e.g. face verification, object detection and/or classification.
Experience training DNN architectures for classification, object detection, and segmentation.
Excellent coding skills in Python. Proficiency with some of these machine vision and machine learning frameworks: OpenCV, TensorFlow + Keras, TensorRT, PyTorch, Pytorch + FastAI.
Good working knowledge of the tools in our dev stack, including Git, Google AI / Vertex AI Cloud, Docker, and Kubernetes. A plus Linux, Redis, and ELK stack.
Solid understanding of statistics, probability, linear algebra & calculus.
Comfortable reading, discussing, and applying research from published papers.
Upholding and promotion of good practices in code design, quality and security.
Communication is important so we expect you to be able to translate complex ideas into understandable content. A pro-active, self-managing attitude. Keen to take ownership for delivering complex projects, from design to deployment to operation.
What we offer in return
Learning days. You can learn during working hours.
We encourage the dissemination of knowledge both through internal meetings and by sharing our experiences with the community. Feel free to propose talks, open spaces, workshops, etc.
Training budget for personal and team formation.
Free day your birthday.
Flexible working hours in a remote-first company. However we do have the possibility to attend co-workings in various locations.
Competitive base salary. Additional year end bonus can be offered based on individual performance and company performance.
About Alice
Alice is a biometric identity verification solution that allows the online onboarding of new clients, reducing identity fraud and maximizing conversion rate. Alice offers a frictionless user's identity verification in a two-step process: user takes a selfie and captures his ID card, Alce does the rest.
Alice Biometrics, as a spin-off from the R&D Technology Center Gradiant, was born with the mission of developing the best-in-class onboarding identity verification solution that uses Deep-Learning based Face Recognition and Passive Liveness Detection technology.
We use a lot of exciting technology. This is our technology stack:
Python for our service back-end code.
RabbitMQ and ELK stack for events queue management, observability and visual representation.
Domain Driven Design as main principle to domain modeling and keep focus on the product.
Test Driven Development to encourage the Outside-In design and improve the quality of our code.
Github for repositories management.
Github Actions for Continuous Integration and Continuous Deployment.
Notion for project management and documentation.
Kubernetes, Docker and Helm to orchestrate our services, KEDA, Prometheus, Grafana, APM.
Google AI Cloud and Kong for underlying infrastructure.
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