Lead/Staff Engineer - Applied AI

This is a high-autonomy engineering role focused on building LLM-powered AI agents and next-generation Generative AI systems at significant scale.
You will lead the development of intelligent systems that power customer-facing automation, personalized communication, scheduling, and operational workflows.
The role sits at the intersection of applied AI, agent architecture, data science, and production engineering.
You will take ambitious ideas from research and experimentation through deployment, monitoring, and continuous iteration.
The position offers the opportunity to work with large-scale data, retrieval systems, foundation models, and sophisticated agent architectures.
You will collaborate closely with product, backend, infrastructure, and engineering teams to create reusable and scalable AI capabilities.
As a technical leader, you will also influence AI strategy, establish best practices, and mentor engineers in a fast-moving, globally distributed environment.

• 8+ years of experience in Data Science, Machine Learning, Applied AI, or a closely related field, with a demonstrated history of delivering production-grade AI and ML systems.
• Hands-on expertise with LLM technologies, including fine-tuning, prompt engineering, embeddings, function-calling agents, and model evaluation.
• Strong experience designing and implementing RAG systems using vector databases such as FAISS, Pinecone, or Weaviate.
• Experience working in cloud-native environments, particularly GCP and AWS, with experience deploying models using technologies such as PyTorch, Hugging Face Transformers, and MLOps tooling.
• Experience with LangChain or similar agent orchestration frameworks, including the ability to design multi-step, tool-augmented agents.
• Strong Python programming skills combined with sound engineering practices around CI/CD, testing, version control, and production software development; familiarity with TypeScript is also valuable.
• Strong foundation in core data science concepts, including supervised and unsupervised learning, causal inference, statistical testing, segmentation, and time-series forecasting.
• Proven ability to take AI and ML solutions from experimentation and prototyping through production deployment, monitoring, observability, and iterative improvement.
• Strong understanding of experimentation and evaluation methodologies for AI systems, including performance measurement, feedback loops, and real-world effectiveness.
• Ability to work independently while collaborating effectively across product, engineering, infrastructure, and other cross-functional teams.
• Demonstrated leadership capabilities, including driving initiatives, influencing technical direction, mentoring peers, and operating effectively in a fast-paced environment.
• Strong product sense and communication skills, with the ability to connect complex technical considerations to product objectives and customer needs.