As a Research Analyst, you will implement classical ML models and LLM based inferences to solve defined problems. You'll work closely with senior scientists and engineers, contributing to model development, evaluation, and optimization.
Key job responsibilities
- Implement machine learning models (classical ML covering classification and regression models) using Python and ML frameworks
- Develop and iterate prompts for LLMs using few-shot, CoT, and instruction-based techniques
- Conduct offline evaluations and benchmarking / testing on LLM and ML outputs
- Collaborate with engineers to deploy models in production and monitor performance
- Document experiments and results; participate and brief milestones in weekly sprint planning and model reviews
Expectations
- Demonstrates hands-on skills in Python and ML/LLM toolkits
- Understands trade-offs between Generative AI vs. Machine Learning approaches
- Can deliver scoped ML components with guidance
- Effective communicator and team collaborator
- Bachelor's degree in a quantitative field, or experience working in Science, Technology, Engineering, or Mathematics (STEM)
- Experience that includes strong analytical skills, attention to detail, and effective communication abilities
- Experience working cross functionally with tech and non-tech teams
- 1+ years of relevant work experience in solving real world business problems using AI/ML or applying GenAI and LLMs through prompt engineering techniques.
- Strong hands-on programming skills in Python, SQL.
- Ability to creatively solve business problems, innovating new approaches where required and articulating ideas to a wide range of audiences using strong data, written and verbal communication skills
- Diverse experiences will be favored eg. a mix of experience across different roles - In-depth understanding of machine learning concepts including developing models and tuning the hyper-parameters, as well as deploying models and building ML service - Technical expertise, experience in AI/ML and GenAI
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