Lead technical strategy for AI/LLM systems
Software Engineering, IT, Data Science
Posted on Aug 14, 2026
Lead / Principal AI & LLM Engineer
CornerStone 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
This opportunity may align with professionals located near one of several major U.S. technology and delivery hubs, including:
Candidates located near one of these markets may be especially well positioned for consideration.
What You'll Do
We're particularly interested in engineers with meaningful production experience in several of the following areas:
Experience with a combination of:
The strongest candidate will be someone who can confidently say:
"I've personally designed and shipped production LLM systems—not just proofs of concept."
You should be comfortable discussing architectural decisions around:
This role is particularly well suited for a Staff Engineer, Principal Engineer, Lead AI Engineer, Generative AI Architect, Applied AI Engineer, or Senior Machine Learning Engineer who wants significant technical ownership while remaining hands-on.
Why Work With CornerStone?
At CornerStone, we specialize in connecting highly skilled technology professionals with meaningful opportunities where their expertise can make an impact.
Our recruiting team works to understand more than keywords on a résumé. We focus on the technologies you've actually worked with, the systems you've built, the problems you've solved, and the type of work you want to tackle next.
For highly specialized technology professionals, that means:
If you've been building real-world Generative AI and LLM systems and want an opportunity where you can influence architecture while continuing to write production code, we'd like to hear from you.
CornerStone 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
This opportunity may align with 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.
What You'll Do
-
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 LangGraph, LangChain, 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, NoSQL, 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.
-
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 applications and services.
-
Experience designing and developing RESTful APIs and distributed systems.
-
Hands-on experience with machine learning frameworks such as:
-
PyTorch
-
TensorFlow
-
Scikit-learn
-
MLflow
-
or comparable technologies
-
-
Strong understanding of statistical analysis, data exploration, feature engineering, preprocessing, and large-scale datasets.
-
Strong SQL skills and experience working with relational databases.
We're particularly interested in engineers with meaningful production experience in several of the following areas:
-
Large Language Models and Generative AI
-
Retrieval-Augmented Generation (RAG)
-
Agentic AI and autonomous/multi-step workflows
-
LangGraph, LangChain, or similar orchestration frameworks
-
Prompt and context engineering
-
Tool/function calling
-
Structured LLM outputs
-
Embeddings and semantic search
-
Hybrid retrieval and reranking
-
Vector databases such as Qdrant, Pinecone, Weaviate, Milvus, pgvector, or similar
-
LLM evaluation and benchmarking
-
AI observability and tracing
-
Guardrails and responsible AI
-
Model routing and model selection
-
Token, latency, and inference-cost optimization
-
Production AI monitoring
-
ML/LLM deployment and inference pipelines
Experience with a combination of:
-
AWS and/or Google Cloud Platform
-
AWS Bedrock or comparable managed Generative AI platforms
-
Kubernetes
-
Docker
-
Kafka or similar event-streaming technologies
-
Airflow or related workflow orchestration platforms
-
Git
-
CI/CD pipelines
-
Automated testing frameworks
-
SQL and modern data-storage technologies
-
Pandas
-
NumPy
The strongest candidate will be someone who can confidently say:
"I've personally designed and shipped production LLM systems—not just proofs of concept."
You should be comfortable discussing architectural decisions around:
-
RAG versus long-context approaches
-
Chunking and document-processing strategies
-
Embedding and retrieval design
-
Vector search versus hybrid search
-
Reranking
-
Agentic workflows and tool execution
-
LLM evaluation methodology
-
Hallucination and grounding
-
Model selection
-
Context management
-
Latency and throughput
-
AI observability
-
Security and responsible AI
-
Cloud architecture
-
Production reliability
-
Cost optimization
This role is particularly well suited for a Staff Engineer, Principal Engineer, Lead AI Engineer, Generative AI Architect, Applied AI Engineer, or Senior Machine Learning Engineer who wants significant technical ownership while remaining hands-on.
Why Work With CornerStone?
At CornerStone, we specialize in connecting highly skilled technology professionals with meaningful opportunities where their expertise can make an impact.
Our recruiting team works to understand more than keywords on a résumé. We focus on the technologies you've actually worked with, the systems you've built, the problems you've solved, and the type of work you want to tackle next.
For highly specialized technology professionals, that means:
-
A recruiting partner who understands technical roles and emerging AI skill sets
-
Clear communication throughout the hiring process
-
Opportunities aligned with your technical background and career direction
-
Representation that highlights your actual technical accomplishments—not simply a list of technologies
-
Access to enterprise technology initiatives where experienced engineers can have meaningful impact
If you've been building real-world Generative AI and LLM systems and want an opportunity where you can influence architecture while continuing to write production code, we'd like to hear from you.