Shape the future of intelligent software by leading the technical direction of core Generative AI systems. In this role, you'll architect and govern evaluation methodologies for both offline and online validation, ensuring robust performance of autonomous agent systems. You'll drive advancements in Retrieval-Augmented Generation (RAG) pipelines, refine information retrieval strategies, and lead prompt engineering initiatives to transform Text2Action concepts into seamless conversational interfaces.
What You’ll Do
- Partner with product and business teams to align data science efforts with strategic goals, identifying high-impact opportunities in the GenAI space.
- Define and implement performance metrics that capture real user value from AI-driven features.
- Design intelligent systems using advanced machine learning, statistical modeling, and mathematical principles.
- Build scalable data processing infrastructure to handle large-scale datasets using distributed computing frameworks.
- Work closely with ML engineers to develop architectures that support real-time AI services.
- Own the full lifecycle of machine learning pipelines—from data preparation and model development to validation and feedback integration.
What We’re Looking For
- Bachelor’s degree or higher in Computer Science, Statistics, Mathematics, or a related field, with at least 9 years of relevant experience.
- Strong programming skills and deep understanding of machine learning fundamentals, including probability, linear algebra, optimization, and statistical inference.
- Proven experience deploying machine learning models in production environments with significant individual contributions.
- Familiarity with natural language processing, LLMs, transformers, knowledge graphs, and prompt engineering is highly valued.
- Experience in time series analysis, distributed systems, large-scale computing, and Big Data tools like Spark is beneficial.
- Background in at least two of: NLP, statistical ML, graph algorithms, deep learning, constraint optimization, or distributed systems.
- Proficiency with both SQL and NoSQL databases, including schema design and query optimization.
Technology Environment
Work with cutting-edge tools and frameworks including Hadoop, Spark, Large Language Models, Transformers, RAG architectures, prompt engineering techniques, knowledge graphs, and distributed data systems.
Our Culture
We prioritize people in every decision, especially when building AI. Our environment supports inclusion, equal opportunity, and personal growth. We believe diverse perspectives lead to stronger teams and better outcomes. Everyone is empowered to pursue their potential, purpose, and passion in a collaborative, welcoming setting.
