Data Scientist (AI & LLM Specialist) - to 240k + bonus + equity (SK)

Salary: $180k to $240k + bonus + equity

We are seeking a highly skilled Data Scientist with a focus on Large Language Models (LLMs) and Machine Learning (ML) to join our team. The ideal candidate will have deep experience in developing and deploying AI solutions, particularly using LLMs such as GPT-3, BERT, or similar technologies, to solve complex business problems. You will work closely with cross-functional teams to design, build, and deploy data-driven models that leverage advanced language processing techniques and traditional ML methods to drive innovation and business success.

Key Responsibilities:

Large Language Model (LLM) Development:

• Design, develop, and deploy LLM-based applications using technologies like GPT-3, BERT, or similar.
• Implement AI solutions using LLMs to automate and optimize business processes, such as text generation, summarization, sentiment analysis, and document classification.
• Fine-tune LLMs to improve model accuracy and efficiency for business-specific tasks.

Machine Learning Model Development:

• Develop and deploy machine learning models using various algorithms (e.g., regression, classification, clustering) and deep learning techniques.
• Integrate LLM-based solutions with traditional machine learning models for complex, multi-faceted business problems.

Algorithm Development and Optimization:

• Build and optimize algorithms to handle large-scale data and improve model performance.
• Leverage ML frameworks such as TensorFlow, PyTorch, and Scikit-learn to implement and fine-tune models, with a particular focus on language processing tasks.

Python Programming and Software Development:

• Write clean, efficient, and scalable Python code for LLM and machine learning model development.
• Utilize libraries such as Pandas, NumPy, and SciPy to manipulate and analyze data, ensuring the smooth integration of models into production environments.

Model Evaluation and Validation:

• Evaluate model performance using appropriate metrics (e.g., accuracy, precision, recall, F1 score) and ensure robustness, reliability, and generalizability of LLM and ML models.
• Continuously improve model outcomes by iterating on performance and fine-tuning for specific use cases.

Collaboration and Reporting:

• Work closely with business leaders, engineers, and other stakeholders to translate business requirements into LLM and ML solutions.
• Present findings and model results to non-technical stakeholders, ensuring actionable insights.
• Create and maintain detailed documentation for processes, models, and methodologies.

Requirements:

• Proven experience as a Data Scientist or Machine Learning Engineer with a focus on Large Language Models (LLMs) such as GPT, Claude, Grok, Gemini, Mistral, LLaMA, Command, Yi, PaLM, Mixtral, Cohere or any others.
• Strong proficiency in Python and experience with libraries such as Pandas, NumPy, TensorFlow, PyTorch, and Hugging Face Transformers.
• Experience in deploying and fine-tuning LLMs (e.g., GPT-3, BERT) for various NLP tasks such as text generation, summarization, sentiment analysis, and document processing.
• Solid understanding of machine learning algorithms and AI techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning.
• Experience with data visualization tools (e.g., Matplotlib, Seaborn, Plotly) for presenting insights and findings.
• Experience with cloud platforms and big data technologies (e.g., AWS, Google Cloud, Hadoop, Spark) is a plus.
• Familiarity with version control systems like Git.
• Bachelor s or Master s degree in Computer Science, Data Science, Mathematics, or a related field.

Preferred Skills:

Experience with LLM Integration:

• Hands-on experience in building end-to-end pipelines for LLM-based solutions, integrating models into larger workflows, and deploying them in production environments.
• Familiarity with frameworks such as Hugging Face, OpenAI API, or similar tools for LLM development and deployment.
• Experience in advanced NLP tasks, including Named Entity Recognition (NER), sentiment analysis, text classification, and question answering.
• Strong communication skills with the ability to explain complex LLM and ML concepts to non-technical audiences.
• Familiarity with agile methodologies and working in a collaborative team environment.