Model Development: Develop, train, and deploy predictive models that enhance the capabilities of our AI solutions.
AI Technologies: Work with state-of-the-art AI techniques, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), and apply them to real-world business contexts.
Cross-Functional Collaboration: Partner with business stakeholders to understand objectives and translate them into actionable machine learning tasks.
Model Optimization: Continuously monitor, evaluate, and improve models based on real-world performance and evolving business needs.
Data Pipeline Maintenance: Implement and manage robust data preprocessing pipelines to ensure high-quality, reliable input data for model development.
What We're looking for:
Master's or Ph.D. in AI, Data Science, Computer Science, Statistics, or a related field.
5+ years of experience in AI, Machine Learning, Reinforcement Learning (RL), Data Science, or a related field.
Proven experience with Large Language Models (LLMs) and parameter-efficient fine-tuning methods (LoRA, QLoRA, etc.) techniques.
Strong proficiency in programming languages like Python. Preference for candidates with a competitive coding background (e.g., ACM/ICPC, NOI/IOI, Top Coder, Kaggle).
Experience in building and deploying LLM-powered applications using FastAPI/Flask and Streamlit for front-end interfaces, solid understanding of REST principles, HTTP Methods, websockets and API lifecycle management.
Experience with SQL, NoSQL (PostgreSQL, MySQL, MongoDB, Redis) and vector databases (Pinecone, Weaviate, pgvector) for structured data storage and semantic search.
Demonstrated success in applying data science and machine learning to solve real-world, business-critical problems.
Excellent communication and collaboration skills with the ability to work across interdisciplinary teams.
Strong communication, stakeholder management, and decision-making skills, with a passion for building diverse, inclusive engineering teams.
