Research, develop, and implement novel Generative AI solutions tailored to telematics applications.
Design and develop generative AI models using advanced machine learning and deep learning techniques.
Implement LLM-powered solutions, employing techniques like prompt engineering and Retrieval Augmented Generation (RAG)-based systems.
Analyze experimental results to evaluate the effectiveness and accuracy of Generative AI algorithms.
Build evaluation systems to assess model output quality and product experience objectively.
Design architectures that meet product requirements, balancing considerations such as AI safety and latency.
Work with software developers, AI engineers, and stakeholders to integrate generative AI solutions into telematics platforms.
Provide project leadership as a subject matter expert (SME), guiding team members and participating in cross-departmental projects.
Mentor team members on best practices for AI product development.
Generate detailed reports and presentations to communicate the impact and value of generative AI solutions to stakeholders.
Stay updated on industry trends and advancements in generative AI and prompt engineering, integrating best practices into development cycles.
Engage with the AI research community, applying the latest findings and technologies to AI initiatives.
Implement new statistical or mathematical methodologies for data analysis models.
Develop algorithms and predictive models to solve complex business problems.
Conduct causality experiments using A/B tests or epidemiological approaches to identify root issues.
Extract value from big data infrastructure (e.g., Google BigQuery) through complex queries and analyses.
Collaborate with cross-functional teams to seamlessly integrate AI technologies into platforms, ensuring personalized user experiences.
Maintain comprehensive documentation of research, methodologies, model design, and experimental results.
Requirements & Skills:
5-8 years in a Data Scientist or similar role. Writing SQL queries, programming in Python, and building production machine learning models.Experience with transformer models such as BERT and GPT, and pre-LLM NLP models like word2vec or GloVe.
Masters in Data Science, Mathematics, Computer Science, Statistics, or related field.
Experience with OpenAI’s and VertexAI frameworks and APIs
Experience in data augmentation techniques to enhance model training and performance.
Proven experience in developing and deploying generative AI projects in real-world applications, including fine-tuning and distilling models.
Expertise in prompt engineering closed-source models (e.g., GPT) or open-source models (e.g., Mixtral) for specific application needs.
Proficiency in model deployment and cloud services (e.g., GCP, Azure, AWS) for scalable solutions.
Knowledge of data management, including building datasets and evaluating model performance.
Familiarity with advanced AI frameworks and libraries such as PyTorch, TensorFlow, and Langchain.
Solid understanding of machine learning principles, statistical analysis, and predictive modeling.
Ability to ensure ethical considerations in Generative AI, including data privacy, bias detection, and model explainability.
Strong project management skills, capable of leading projects from conception to completion.
Excellent communication skills for effective collaboration across teams and with external partners.
High attention to detail and analytical skills, with the ability to make well-judged decisions.
Strong team player with the ability to engage effectively at all organizational levels.
Business acumen to align technical initiatives with strategic business objectives.
Adaptive mindset to stay current with evolving technologies and market demands.
Entrepreneurial mindset and comfort in a flat organizational structure.
