Build production AI features and services using foundation model APIs, retrieval-augmented generation (RAG), and agentic workflows. You will implement scalable microservices in Python or Java, develop evaluation and quality frameworks for AI systems, and collaborate across teams to integrate AI capabilities into enterprise workflows. The role requires hands-on applied AI experience, strong backend or platform engineering skills, and familiarity with vector databases, LLM orchestration tools, and cloud infrastructure.

Design, build, and ship LLM-powered capabilities end to end—from prototyping and fine-tuning models to deploying production agents and retrieval systems. Own the full stack: prompt and context engineering, multi-step agent design with tool calling, RAG systems (embeddings, chunking, hybrid search, reranking), fine-tuning on multi-GPU with LoRA/QLoRA, evaluation and tracing infrastructure, and clean APIs for other engineers. Work in a secure, distributed environment where the platform runs on customer compute, cloud, or hybrid setups, requiring expertise with both commercial and self-hosted models.

As Principal Software Engineer for AI & Data Platform, you will architect the data foundation for scientific and engineering R&D platforms, designing scalable data processing patterns, ML training pipelines, and intelligent workflow interfaces. You will own end-to-end responsibilities including data modeling for multi-use analytics and ML, building production training and fine-tuning pipelines, model evaluation and benchmarking, and setting engineering standards for the team. The role requires 10+ years shipping production software, expert-level Python, deep experience with large-scale data systems (object storage, analytical processing, training formats), hands-on ML pipeline development, and the ability to set technical direction in early-stage environments while implementing it yourself.

Build the foundational agentic AI layer for a materials-science platform, including multi-model provider abstraction, agent orchestration with stateful checkpoints, retrieval systems, prompt versioning, and comprehensive tracing and evaluation frameworks. You'll design agents that plan and reason over tool calls in production, implement human-in-the-loop safety gates, and ensure all LLM behavior remains auditable and cost-tracked across customers' secure environments. The role demands deep production experience with agentic and LLM systems: async Python, structured outputs, memory and context management, multi-step workflow orchestration, and evaluation harnesses that catch regressions before deployment.