Architect and Implement AI Systems: Lead the design and implementation of intelligent systems that go beyond a single model. Youll focus on delivering production-ready features, not just impressive demos, by selecting the right tools for the job—even if that means going beyond Large Language Models.
Data Preparation and Curation: Prepare and curate high-quality datasets for modeling, keeping data quality at the center of all experimentation and delivery. Youll also design and build our feature store to ensure data and features are consistently and reliably available for both training and inference.
Build Robust Data and ML Pipelines: Design and maintain end-to-end data and machine learning pipelines, from data ingestion to model deployment and monitoring. This includes building our analytics infrastructure with reusable dbt models and designing scalable pipelines using Airflow.
Develop and Deploy Models: Take a hands-on role in developing and training models, ensuring they are performant, reliable, and reproducible. You will design, explore, and prototype solutions using various neural network architectures, including and beyond LLMs.
Implement Advanced Retrieval Systems: Explore and implement solutions like Retrieval-Augmented Generation (RAG) or Graph RAG to improve the quality of information retrieval and reasoning in LLM workflows. This includes graph construction, entity linking, and hybrid scoring strategies.
Focus on On-Device Intelligence: Quantize large models into smaller, more efficient models to enable edge intelligence and on-device processing where appropriate.
Collaborate and Strategize: Work cross-functionally to define tracking schemas and event-level data that power both analytics and AI/ML initiatives. Youll also contribute to our AI strategy and roadmap, helping shape how we scale models responsibly across the platform.
requirements-expected :
End-to-End Ownership: Youre a full-stack engineer, comfortable with the entire lifecycle of an AI component, from planning and modeling to testing and continuous improvement. You are capable of delivering an entire AI use case from inception to production.
Modeling Expertise: You have experience designing, evaluating, and prototyping a variety of models. You are proficient in Python and have expertise in ML frameworks like SciKit-Learn, TensorFlow, PyTorch, and the Hugging Face ecosystem.
MLOps and Data Proficiency: You have strong experience in MLOps foundations and tools, including MLflow and ZenML. You also have proven experience in dbt modeling and structuring data in both data warehouses and data lakes.
Pipeline and Data Skills: You have hands-on experience building and scheduling data pipelines in Airflow and experience defining tracking schemas and event-level data for reliable analytics. You are familiar with modern data stacks (e.g., Kafka, Spark, or cloud data warehouses like BigQuery, Redshift, or Snowflake).
Problem-Solving & Evaluation: You understand model behavior and output evaluation. You are capable of developing evaluation frameworks for testing model output quality, reliability, and alignment with user goals (e.g., hallucination detection, prompt regression, safety scoring). You are also familiar with RAG, Graph-based retrieval, prompt design, and multi-hop reasoning.
Practical Mindset: You are committed to building things that are reliable, explainable, and usable by others.
