About Avolta:
Avolta is a leading innovator in security solutions, dedicated to protecting critical assets and ensuring safety across various industries. With a strong focus on automotive security, we specialize in developing cutting-edge technologies, such as advanced anti-theft systems, to safeguard vehicles and enhance driver and passenger safety. By leveraging artificial intelligence, machine learning, and computer vision, we deliver innovative solutions that address complex security challenges in the automotive sector and beyond. Our team is composed of highly skilled professionals who are committed to excellence, integrity, and the mission of creating a safer future.

Position Overview:
Avolta is seeking a skilled and experienced Part-Time Machine Learning Professional with a focus on computer vision to join our team. This role is ideal for individuals who are looking for a flexible, part-time opportunity to contribute to impactful projects while balancing other commitments. The successful candidate will play a key role in developing and deploying machine learning models, particularly in the field of computer vision, to enhance our security solutions, including automotive anti-theft systems.

Key Responsibilities:

  • Design, develop, and deploy advanced machine learning models with a focus on computer vision applications for security systems, including automotive anti-theft solutions.
  • Collaborate with the team to preprocess data, train models, and evaluate their performance.
  • Participate in research and development activities to explore new techniques and technologies in machine learning and computer vision.
  • Contribute to the optimization and deployment of machine learning models for real-world applications.
  • Document and present project findings and results to the team.
  • Participate in team meetings and brainstorming sessions to share ideas and insights.
  • Uphold the highest standards of professionalism and integrity in all tasks and interactions.

Required Qualifications:

  • Advanced degree (Master’s or Ph.D.) in Computer Science, Machine Learning, Artificial Intelligence, or a related field, or equivalent professional experience.
  • Proven experience in developing and deploying machine learning models, with a strong focus on computer vision.
  • Proficiency in programming languages such as Python and familiarity with libraries like TensorFlow, PyTorch, or OpenCV.
  • Strong understanding of data preprocessing, model training, and performance evaluation.
  • Excellent problem-solving skills and the ability to think critically and creatively.
  • Strong communication and teamwork skills.
  • High level of responsibility, maturity, and a commitment to delivering high-quality work.

Preferred Qualifications:

  • Experience in automotive security, anti-theft technologies, or related fields.
  • Familiarity with cloud computing platforms (e.g., AWS, Azure, Google Cloud).
  • Knowledge of cybersecurity principles and practices.
  • Experience with edge computing and deploying models on embedded systems.

Why Join Avolta?

  • Contribute to a mission-driven organization that plays a critical role in advancing security solutions, particularly in the automotive sector.
  • Collaborate with a team of passionate, talented, and dedicated professionals.
  • Gain the opportunity to work on innovative projects that have a direct impact on security and safety.
  • Flexible part-time schedule to accommodate your other commitments.
  • Potential for future opportunities within Avolta based on performance and interest.

How to Apply:
If you are a skilled professional with a passion for machine learning and computer vision, we invite you to apply for this part-time role. Please submit your application directly on this page by providing your resume, a brief statement of interest, and any relevant project portfolios or GitHub links.

Avolta is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all team members.

Job Category: AI/Machine Learning
Job Type: Part Time
Job Location: In-Person
This job is no longer accepting applications.

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