Data Scientist / AI Modeler

Responsibilities

• Identify and select appropriate pre-trained models for various customer use cases, evaluating model performance and suitability for specific tasks.
• Fine-tune pre-trained models to optimize performance for specific applications, ensuring models meet the desired accuracy, efficiency, and scalability requirements.
• Collaborate with development teams to integrate AI models into mobile and web applications, whether as standalone services, microservices, or lightweight models for mobile deployment.
• Stay up-to-date with the latest advancements in AI, machine learning, and deep learning, and apply this knowledge to develop innovative AI solutions.
• Conduct experiments to test, validate, and compare different models and approaches, using Python, Jupyter Notebooks, and other relevant tools.
• Develop and integrate Retrieval-Augmented Generation (RAG) systems to enhance AI capabilities, such as improving model responses by combining traditional search with AI-generated content.
• Optimize models for performance, including reducing latency, improving inference speed, and minimizing resource consumption.
• Design and execute comprehensive testing strategies to validate the performance and reliability of AI models, ensuring they meet production standards.
• Work closely with cross-functional teams, including software engineers, product managers, and UX/UI designers, to deliver AI-driven features that enhance user experiences.
• Document the AI modeling process, including model selection criteria, fine-tuning parameters, and integration steps.
• Communicate findings and progress to stakeholders.
• Monitor and analyze the performance of deployed models, making adjustments and improvements as needed to ensure optimal results.

Qualifications

• Bachelor's or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field. Ph.D. is a plus.
• 3+ years of experience as a Data Scientist, AI Modeler, or similar role, with a strong track record of deploying AI models in production environments.
• Proficiency in Python and experience with data science libraries such as TensorFlow, PyTorch, Scikit-learn, etc.
• Hands-on experience with Jupyter Notebooks for data analysis, model experimentation, and visualization.
• Experience with cloud platforms such as AWS, Azure, or GCP, and familiarity with deploying models in these environments.
• Expertise in using and fine-tuning pre-trained models for various NLP, computer vision, and other AI-related tasks.
• Strong understanding of model validation techniques, including cross-validation, A/B testing, and statistical analysis.
• Experience with implementing and optimizing Retrieval-Augmented Generation (RAG) systems.
• Familiarity with version control systems such as Git.
• Strong problem-solving and analytical skills, with the ability to think critically about AI model performance and scalability.
• Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams.
• Strong attention to detail and commitment to producing high-quality, reliable AI solutions.
• Able to work independently, manage multiple projects, and meet deadlines.