Quantitative Analytics Program - Applied Computational Intelligence

What you'll do

• Develop and deploy AI and machine learning solutions across generative AI, agentic systems, and traditional models.
• Build LLM-powered agents and multi-agent systems for task orchestration and automated business processes.
• Create enterprise knowledge platforms using Retrieval-Augmented Generation (RAG) and large language models.
• Train, fine-tune, and optimize transformer-based models through techniques like distillation, quantization, and pruning.
• Build and optimize scalable model training and deployment pipelines using distributed computing.
• Monitor production AI systems to evaluate performance, stability, and model drift.
• Apply statistical and quantitative techniques to validate model design, calibration, and implementation.
• Develop AI-powered decision support systems that synthesize complex data into actionable insights.

What we're looking for

• Must have 6+ months of work experience or equivalent training/education.
• Currently pursuing a Master's degree in Computer Science, Machine Learning, AI, Engineering, or a related quantitative field (preferred).
• Expected graduation date must be between December 2026 and June 2027 (preferred).
• Strong programming experience with tools such as Python, R, SQL, Java, Spark, or similar technologies.
• Hands-on experience developing machine learning and AI solutions in research, academic, or industry environments.
• Experience with LLMs, model training (SFT, RLHF, PPO), and deployment in cloud environments like GCP (preferred).
• Knowledge of agentic AI architectures, RAG applications, and optimization techniques like LoRA and PEFT (preferred).
• Strong quantitative skills including data analysis, modeling, visualization, statistics, and research.