Key Responsibilities
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- Architect Solutions: Design and optimize scalable ML systems with a focus on computer vision applications.
- Build & Deploy Models: Develop and productionize deep learning models using Python and contemporary ML frameworks.
- Lead Cross-Functionally: Align research, engineering, and product goals by providing hands-on leadership.
- Mentor & Guide: Support junior team members and promote best practices in ML development.
- Engage Customers: Collaborate directly with enterprise clients to facilitate the seamless integration of AI solutions.
- Drive Innovation: Evaluate and implement recent AI tools and methodologies to enhance performance and scalability.
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Qualifications Required
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- Applied AI Expertise: You should have at least 5 years of experience in building ML systems, particularly in computer vision.
- Robust Python & ML Skills: Proficiency in Python and ML libraries (e.g., PyTorch, TensorFlow) is essential.
- Production Experience: Hands-on experience deploying ML models in production environments.
- Cloud & Data Engineering: Knowledge of cloud platforms (preferably AWS) and scalable data pipelines is required.
- Collaboration & Communication: Ability to work across teams and effectively communicate complex ideas.
- Problem-Solving Mindset: Strong analytical thinking skills with a focus on continuous improvement.
In addition to the above, having experience in applying AI in insurance, automotive, or similar sectors, expertise in large language models or conversational AI, proven success in scaling ML solutions in production, and a track record of mentorship and technical leadership in data science teams would be preferred.
