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Key Responsibilities
•    Work with our center of excellence for GenAI on creating groundbreaking solutions and conquering challenging projects.
•    Build, fine-tune, and optimize locally hosted SLMs using curated golden questions and answers.
•    Leverage expertise in models such as BERT, SBERT, and other transformer architectures to enhance language model performance.
•    Design and execute fine-tuning workflows for both on-premises (NVIDIA A100 GPUs or similar) and cloud-based environments.
•    Develop benchmarking frameworks to track model performance, quantify results, and establish measurable improvement metrics.
•    Identify key parameters to evaluate and improve performance across various value driven use cases.
•    Apply best practices in data refinement and preprocessing to ensure high-quality input for training and fine-tuning.
•    Stay updated with the latest advancements in generative AI and machine learning technologies to incorporate innovative approaches.
•    Collaborate with cross-functional teams, including data scientists, engineers, and non-technical stakeholders, to deliver effective AI solutions.

 

Qualifications
•    Experience: 7+ years in data science and machine learning.
•    Technical Expertise:
o    Proven track record with transformer models (e.g., BERT, SBERT) and generative AI technologies.
o    Experience with LLMs and SLMs, particularly in fine-tuning and deploying on-premises and cloud environments.
o    Familiarity with GPU setups like NVIDIA A100s for model training and optimization.
•    Data Science Skills:
o    Strong fundamentals in data refinement, preprocessing, and quality assurance.
o    Proficient in designing benchmarking processes, tracking performance, and tying results to numerical metrics.
o    Ability to identify and optimize key performance parameters for specific use cases.
•    Communication Skills: Excellent verbal and written communication skills, with the ability to clearly articulate complex technical ideas to diverse audiences.
•    Education: Bachelor's or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.

Preferred Skills
•    Experience with fine-tuning LLMs/SLMs in enterprise environments.
•    1-2 years of research-focused work for LLM / benchmarking.
•    Familiarity with benchmarking tools and frameworks for performance evaluation.