Design, build, train, and fine-tune complex AI and machine learning models, including deep learning, natural language processing (NLP), and generative AI models like Large Language Models (LLMs).
Develop Retrieval-Augmented Generation (RAG) and agentic workflows to enhance model accuracy and performance.
Collaborate with machine learning engineers and DevOps teams to deploy AI models into production and establish MLOps best practices.
Play a key role in understanding data and help unlock the potential of integrating robust and cost optimized AI/ML techniques for training and inference.
Ensure data quality and integrity throughout the data science lifecycle.
Requirements
Bachelor's or Master's degree in Data Science and./or Computer Science, or equivalent
Typically 2-8 years’ experience in a quantitative role navigating ambiguous environments, ideally in infrastructure, systems or platform domains in an enterprise setting.
Development experience with Python, Go or (Java, C#, C++, C) or similar programming languages
Successful with big data, LLM and analytics
Good understanding of distributed systems, event driven programming paradigms and designing for scale and performance
Strong communication skills and ability to work in a distributed team.
Benefits
Health & Wellbeing
Personal & Professional Development
Unconditional Inclusion
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
AI modelsmachine learningdeep learningnatural language processinggenerative AIRetrieval-Augmented GenerationMLOpsPythonGobig data
Soft skills
strong communication skillscollaborationability to work in a distributed team
Certifications
Bachelor's degree in Data ScienceMaster's degree in Data ScienceBachelor's degree in Computer ScienceMaster's degree in Computer Science