Role & responsibilities As a Data Engineer focused on web crawling and platform data acquisition, you will design,develop, and maintain large-scale web scraping pipelines to extract valuable platform data. Youwill be responsible for implementing scalable and resilient data extraction solutions, ensuringseamless data retrieval while working with proxy management, anti-bot bypass techniques, anddata parsing. Optimizing scraping workflows for performance, reliability, and efficiency will be akey part of your role. Additionally, you will ensure that all extracted data maintains high qualityand integrity. Preferred candidate profile We are seeking candidates with: Strong experience in Python and web scraping frameworks such as Scrapy, Selenium, Playwright, or BeautifulSoup. Knowledge of distributed web crawling architectures and job scheduling. Familiarity with headless browsers, CAPTCHA-solving techniques, and proxy management to handle dynamic web challenges. Experience with data storage solutions, including SQL, and cloud storage. Understanding of big data technologies like Spark and Kafka (a plus). Strong debugging skills to adapt to website structure changes and blockers. A proactive, problem-solving mindset and ability to work effectively in a team-driven environment.
Role & responsibilities Lead end-to-end development and deployment of ML and GenAI models into product environments. Manage and mentor a team of data scientists and ML engineers, providing both technical and career guidance. Drive the integration of ML workflows into scalable engineering pipelines. Oversee quality monitoring, performance drift tracking, and continuous improvement of deployed models. Collaborate with cross-functional stakeholders including product, engineering, and design teams to align ML solutions with product goals. Contribute to architectural decisions involving LLMs, RAG systems, and agentic workflows. Maintain technical excellence by staying updated on the latest research in ML and GenAI. Preferred candidate profile Bachelors or Masters degree in Computer Science, Data Science, or a related field. 5 to 7 years of ML experience, including hands-on work with supervised learning, NLP, and production deployment. At least 2 years working directly with LLMs (e.g., GPT-4, Claude, Gemini). Experience with MLOps tooling and practices (CI/CD, monitoring, model versioning). Strong Python skills with libraries such as PyTorch, scikit-learn, and Hugging Face. Proven track record in leading data science projects from ideation to delivery. Strong communication and project management skills