Senior Gen AI Analyst

Role Overview

• partner closely with Product, Sales, Marketing, and Engineering teams to apply cutting edge Gen AI tools and techniques, along with advanced analytics to solve complex problems across prospecting, existing customer, sales and data journeys.
• This role focuses on identifying customer intent, improving decision-making and boosting sales productivity by focusing on generating insights embedded in the sales and customer workflows.
• This role requires deep technical judgment, hands-on modeling experience, and the ability to translate complex customer, sales and behavioral data into scalable machine learning and LLM driven solutions.

Requirements

• PhD-level candidate with strong expertise across analytics and advanced AI/ML and LLM techniques or a Master’s candidate with at least 3 years of work experience in Generative AI/ Advanced ML
• Strong proficiency in Python and SQL, with the ability to implement, test, and iterate on ML / LLM-based solutions efficiently.
• An expert in LLM including prompt engineering, creation of evaluation datasets, understanding LLM capabilities and limitations, and familiarity with LLM performance metrics.
• Knowledge of how to automate or fast track the prompt engineering process via latest techniques (Reinforcement Learning).
• Familiarity using contemporary LLM frameworks and tooling such as LangChain and proven experience executing LLM / GenAI use cases in practical settings, such as prompt engineering, retrieval-augmented generation (RAG), and integration with data pipelines or downstream applications.
• Strong execution capability: Execution-oriented mindset with the ability to translate business questions into working, deployable ML solutions, prioritizing speed, correctness, and practical impact.
• Experience working with modern data and ML tooling, and comfort operating in fast-moving, cross-functional environments.
• Strong analytical and problem-solving skills, with a high level of reliability and accountability in delivering assigned work independently and on schedule.
• Experience with reinforcement learning, fine-tuning, or/and lightweight RL-based approaches applied to LLMs or related models is a strong plus.
• Familiarity with LLM agent design patterns, including debugging, monitoring, or iterating on multi-step or long-running executions in real-world environments is a plus.
• Some research or exploratory experience (e.g., prototyping, experimenting with new techniques, reading and applying recent work), while maintaining a strong focus on execution and delivery, is a plus.

Tech Stack

• Python
• SQL