Lead technical strategy for AI​/LLM systems

Lead / Principal AI & LLM Engineer

Corner Stone is seeking a hands-on Lead / Principal AI & LLM Engineer to help architect and build the next generation of production-grade AI systems. This is an opportunity for an experienced AI engineer who wants to remain deeply technical while influencing architecture, engineering standards, and the direction of enterprise Generative AI initiatives. We're looking for someone who has gone beyond AI prototypes and experimentation—someone who has designed, built, deployed, evaluated, and operated LLM-powered applications in production.

The ideal candidate combines strong software engineering fundamentals with deep recent experience across Generative AI, retrieval-augmented generation (RAG), agentic systems, LLM evaluation, Python backend development, cloud infrastructure, and distributed systems.

Potential work locations include professionals located near one of several major U.S. technology and delivery hubs, including:

• Richardson, Texas — U.S. headquarters and major technology center
• Phoenix, Arizona — Innovation and delivery center
• Indianapolis, Indiana — Large-scale technology and training hub
• Raleigh, North Carolina — Technology and innovation workforce center
• Hartford, Connecticut — New England client relationship hub
• Providence, Rhode Island — Technology corridor and startup hub
• Atlanta, Georgia — Business process and delivery center
• Austin, Texas — Office and development location
• Bellevue, Washington — Regional technology office
• New York, New York — Corporate and client engagement presence

Candidates located near one of these markets may be especially well positioned for consideration.

Lead the technical architecture and engineering strategy for production AI and LLM-powered applications across multiple initiatives. Architect scalable RAG, retrieval, orchestration, agentic AI, tool-calling, and LLM evaluation systems. Design AI applications that balance accuracy, reliability, latency, scalability, security, and cost. Build and maintain production-grade Python services, APIs, data pipelines, and AI application components. Design systems for ingesting, validating, chunking, transforming, enriching, indexing, and retrieving structured and unstructured enterprise data.

Develop retrieval architectures incorporating embeddings, semantic search, reranking, metadata filtering, hybrid search, and vector databases. Build and improve agentic workflows and multi-step AI orchestration using frameworks such as Lang Graph, Lang Chain, or equivalent technologies. Establish approaches for LLM evaluation, observability, tracing, testing, hallucination reduction, guardrails, and responsible AI deployment. Design evaluation frameworks covering retrieval quality, response quality, groundedness, relevance, safety, latency, and cost.

Evaluate emerging models, frameworks, infrastructure, and AI development techniques and determine which technologies are appropriate for production adoption. Integrate AI applications with enterprise systems through REST APIs, event-driven architectures, messaging platforms, and distributed services. Design and optimize data models and storage solutions using relational, No

SQL, and vector database technologies. Build reliable AI/ML deployment and inference workflows using modern cloud and container infrastructure. Establish strong software engineering practices around automated testing, CI/CD, version control, monitoring, and deployment. Mentor engineers and help elevate the team's capabilities in AI engineering, architecture, and AI-native software development. Remain hands-on and regularly contribute production-quality code.

Core qualifications include a bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or another quantitative/technical discipline. Approximately 8–12+ years of software engineering, machine learning, data engineering, or related technical experience. Strong recent experience building production AI/ML or Generative AI systems. Advanced proficiency with Python and the Python data/AI ecosystem. Strong software engineering background with experience building scalable backend…