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Job Overview
The Spec Analytics Analyst is a developing professional role. Applies specialty area knowledge in monitoring, assessing, analyzing and/or evaluating processes and data. Identifies policy gaps and formulates policies. Interprets data and makes recommendations. Researches and interprets factual information. Identifies inconsistencies in data or results, defines business issues and formulates recommendations on policies, procedures or practices. Integrates established disciplinary knowledge within own specialty area with basic understanding of related industry practices. Good understanding of how the team interacts with others in accomplishing the objectives of the area. Develops working knowledge of industry practices and standards. Limited but direct impact on the business through the quality of the tasks/services provided. Impact of the job holder is restricted to own team.
Key Responsibilities –
Model Development: Assist in the development, fine-tuning, and deployment of generative AI models and large language models.
Data Preparation: Clean, preprocess, and organize large datasets for training and evaluation purposes.
RAG Frameworks – Customize and fine-tune existing / new RAG frameworks to meet project requirements.
Research and Experimentation: Conduct experiments to test and validate model performance and keep up to date with the latest advancements in the field of AI and NLP.
Performance Optimization: Implement techniques to improve model efficiency, accuracy, and scalability.
Collaborative Projects: Work with cross-functional teams to integrate AI models into various applications and services.
Documentation: Maintain clear and comprehensive documentation of model architectures, processes, and findings
Qualifications –
Bachelor’s degree in computer science, Data Science, Electrical Engineering, or a related field. A master's degree is a plus.
Programming Skills: Proficiency in programming languages such as Python, and familiarity with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, Hugging Face Transformers).
Mathematics and Statistics: Strong understanding of fundamental concepts in mathematics and statistics, including linear algebra, calculus, and probability.
NLP Knowledge: Basic knowledge of natural language processing techniques and concepts, such as tokenization, embeddings, and sequence models.
Problem-Solving Skills: Ability to analyze complex problems, develop innovative solutions, and effectively communicate findings.
Teamwork: Strong collaborative skills and a willingness to work in a dynamic team environment.
Curiosity and Learning: Eagerness to learn and stay current with the latest research and trends in generative AI and LLMs.
Preferred Skills –
Previous experience with AI/ML projects, internships, or relevant coursework.
Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) and version control systems (e.g., Git).
Experience with deploying machine learning models in a production environment.
Strong communication skills, both verbal and written, and the ability to explain technical concepts to non-technical stakeholders.
Education:
Bachelors/University degree or equivalent experience
This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.