Role Description As a Forward Deployed ML/AI Engineer, you will bridge the gap between cutting-edge AI research and robust, production-grade applications. You will be responsible for the end-to-end lifecycle of intelligent systems, from data ingestion and model training to deployment. This role requires deep technical proficiency in: - Training and tuning classical machine learning models (such as gradient-boosted trees, random forests, and regression suites) - Modern Generative AI architectures, including Large Language Models (LLMs), retrieval-augmented generation (RAG) pipelines, and agentic workflows Your mission goes beyond model development—you will own the end-to-end delivery of intelligent systems that directly impact customer business metrics. You will: - Design scalable APIs - Optimize model inference latency - Architect full-stack infrastructure to ensure AI capabilities are seamlessly delivered to end-users This role requires exceptional ability to navigate ambiguity, build trust with diverse stakeholders, and operate effectively in fast-paced, cross-functional customer environments. Qualifications - 6+ years of Machine Learning, SWE Gen AI or DS experience (must have productionized models) - 3+ years of implementation and customer-facing experience - In-depth knowledge of classical models (Scikit-Learn, XGBoost) and Generative AI architectures (LLMs, RAG pipelines, and Vector Databases) - Strong engineering skills in backend development (Python, FastAPI/Flask) and ML frontend frameworks (Streamlit) - Proven experience deploying, monitoring, and maintaining models in production (Docker, CI/CD pipelines) - Ability to translate business challenges into clear technical solutions, focusing on business outcomes and identifying root causes - Ability to explain technical concepts and trade-offs to executives in clear business terms - Experience building trust with customers, managing stakeholders, and working independently in fast-paced, ambiguous environments - Deep familiarity with tools like MLflow or Weights & Biases to track experiments, manage model packaging, and maintain an organized model registry - Experience setting up and managing AI/ML environments on cloud platforms (AWS, GCP, or Azure) - Background in building data pipelines, ETL processes, and working with SQL/NoSQL databases Requirements - Familiarity with reducing inference latency and managing compute costs (e.g., quantization, caching strategies) - Experience building autonomous AI agents or multi-agent orchestration frameworks Benefits - Ownership through equity participation - Annual company retreat - Education bonus for continuous learning - Company-wide winter break - Paid time off - Optional in-person events and meetups - Tailored career roadmaps - High-performance culture