Lead/Staff Engineer - Applied AI
Role Overview
We're seeking a seasoned Engineer to lead the development of LLM-powered AI agents and next-gen Generative AI systems that drive core product functionality and customer-facing automation at scale.
This is a high-autonomy, high-impact role for someone who thrives at the intersection of applied AI, agent design, and core data science.
You'll build foundational models, retrieval systems, and dynamic agents that interact with millions of users — powering personalized communication, intelligent scheduling, smart replies, and much more.
We are looking for builders who can take projects from research and experimentation to production and iteration, and who bring strong data science rigour alongside hands-on GenAI experience.
Key Skills for This Role
Full Job Posting
EEO Statement
The company is an Equal Opportunity Employer. As an employer subject to affirmative action regulations, we invite you to voluntarily provide the following demographic information. This information is used solely for compliance with government recordkeeping, reporting, and other legal requirements. Providing this information is voluntary and refusal to do so will not affect your application status. This data will be kept separate from your application and will not be used in the hiring decision. We encourage you to review our Privacy Policy before submitting your application
Requirements
- 8+ years of experience in Data Science, Machine Learning, or Applied AI, with a track record of delivering production-grade models and systems
- Hands-on expertise with LLMs: fine-tuning, prompt engineering, function-calling agents, embeddings, and evaluation techniques
- Strong experience in building retrieval-augmented generation (RAG) systems using vector databases (e.g., FAISS, Pinecone, Weaviate)
- Experience working in cloud-native environments (GCP, AWS) and deploying models with frameworks like PyTorch, Transformers (HF), and MLOps tools
- Experience with LangChain or similar agent orchestration frameworks; ability to design multi-step, tool-augmented agents
- Proficiency in Python, with strong engineering practices (CI/CD, testing, versioning) and familiarity with TypeScript
- Solid foundation in core data science: supervised and unsupervised learning, causal inference, statistical testing, segmentation, and time-series forecasting
- Proven experience taking ML/AI solutions from prototype to production, including monitoring, observability, and model iteration
- Ability to work independently and collaboratively, leading initiatives and mentoring peers in a fast-paced, cross-functional environment
- Strong product sense and communication skills—able to translate between technical constraints and product goals
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