About the team

The Careem Data Science team’s mission is to drive competitive value from data at scale by building AI models to optimize user experiences, decision-making, and operational efficiencies, and lead the region’s AI ecosystem. As one of the tech leaders in this team, you will be at the forefront of fulfilling this mission. You will be working with the top data science talent of the region while innovating on our user experience using GenAI.

What you'll do

• Collaborate in building a long-term vision of how we can rethink GenAI at Careem
• Drive exploratory analysis to understand the ecosystem, and user behavior; identify new levers to help move metrics and build models of user behaviors for analysis and product enhancements using GenAI
• Shape and influence models and instrumentation to optimize the product experience and generate insights on new areas of opportunity and new products.
• Provide product leadership by sharing data-based recommendations to communicate the state of business, the root cause of change in metrics, and experimentation results influencing product and business decision
• Implement scalable machine learning GenAI solution  that will be used in production on big data.
• Design and run randomized controlled experiments, analyze the resulting data and communicate results with other teams.
• You will always challenge the status quo and continually investigate new data processing technologies and seek to ensure that we follow the industry best practices.
• Build and deploy retrieval augmented generation systems and other applications of large language models.
• Collaborate with cross-functional teams including data scientists, product managers, and domain experts to deliver AI-driven solutions.

What you'll need

• 4-6 years of experience in machine learning, software engineering, Big Data methodologies, transformation and cleaning of both structured and unstructured data.
• Advanced degree in a quantitative discipline such as Physics, Statistics, Mathematics, Engineering or Computer Science.
• Strong understanding of transformer architectures, attention mechanisms, and recent advancements in Large Language Models (LLMs)
• Experience with advanced prompting techniques, including Chain of Thought (CoT) prompting, in-context learning, and few-shot learning.
• Proficiency in using LangChain and LangChain Expression Language (LCEL) for building complex pipelines and workflows with LLMs.
• Experience in developing observable LLM-powered compound systems through tracing to monitor performance and behavior in production environments.
• Experience with one of the following machine learning frameworks: PyTorch or TensorFlow.
• Knowledge of distributed training frameworks (e.g., DeepSpeed, Megatron-LM) and optimizing model performance using techniques like mixed-precision training, gradient checkpointing, and model parallelism would be advantageous.
• Experience with sequence-to-sequence models, self-supervised learning techniques, and understanding NLP concepts such as tokenization, parsing, and semantic analysis.
• Proficiency in creating scalable and maintainable APIs using FastAPI or similar frameworks.
• Strong understanding of good software engineering practices, including code versioning (e.g., Git), CI/CD pipelines, and automated testing.
• Experience with both SQL and NoSQL databases for managing training data and model artifacts.
• Proficiency in Python, SQL, and familiarity with data processing frameworks like Spark and Hive.
• Knowledge of classic ML and DL