Principal AI Engineer

GSquared Group; Seattle (WA); On-site; USD 180,000 - 240,000; Full time

Job summary

GSquared Group is seeking a Principal AI Engineer to define the company-wide AI strategy, architecture, and technical direction. You will lead the design, build, and deployment of production-grade AI systems, including LLM apps, predictive models, and intelligent automation that improve speed, accuracy, and scale.

The role collaborates with Software, Product, Data, Security, and Operations to ensure high-quality, cost-efficient, and compliant AI solutions.

Qualifications

• Bachelor’s degree in CS/Engineering or equivalent.
• 10+ years software engineering with 6+ years in production AI/ML.
• Proven experience delivering enterprise-scale AI systems with measurable impact.
• Expert Python and enterprise languages like C# or Java.
• Deep hands-on expertise with LLMs, retrieval augmentation, embeddings, vector search, and evaluation.
• Experience operating AI workloads on AWS, Azure or comparable.
• Strong APIs, microservices, distributed systems, SQL, Git, CI/CD, containers, and testing knowledge.
• Demonstrated enterprise-level technical leadership and strategic direction.
• Experience with agentic coding tools and multi-step agent workflows.
• Understanding of LLM behavior in production (latency, cost).
• Excellent communication to influence executives and engineers.

Responsibilities

• Define multi-year AI strategy and roadmap aligned to business priorities.
• Architect large-scale AI/ML solutions across platforms and products.
• Design LLM applications using retrieval augmentation, prompts, structured outputs, and agents.
• Establish patterns for agentic automation and human review checkpoints.
• Identify emerging AI tech and lead proofs of concept to scalable implementation.
• Determine the right solution approach per problem (prompting, retrieval, fine-tuning).
• Own enterprise AI reference architecture, platform strategy, and standards.
• Evaluate vendors, make build-versus-buy recommendations, and guide cloud relations.
• Define data contracts, features, vector stores, and lineage with Data Eng.
• Lead error analysis, observability, and regression testing for AI releases.
• Mentor engineers and lead cross-functional AI initiatives.
• Ensure responsible AI, security, and compliance in deployments.

Skills

LLMs

Production ML

Python

C# / Java

AWS / Azure

APIs & microservices

Distributed systems

CI/CD

Agent frameworks

MLOps tooling

Leadership

Communication

Education

Bachelor’s degree in CS/Engineering or equivalent

Master’s preferred

Tools

Docker

Kubernetes

LangChain

LangGraph

LlamaIndex

Semantic Kernel

GitHub Copilot

Job description

The Principal AI Engineer is the senior-most individual contributor responsible for defining AI strategy, architecture, and technical direction across the organization. This role leads the design, build, and deployment of production-grade AI systems — including large language model applications, predictive models, intelligent document processing, and agentic automation — that improve speed, accuracy, and scale across complex business workflows.

This position sets enterprise-wide AI standards, solves highly ambiguous technical problems, and acts as a trusted advisor to engineering and executive leadership. The Principal AI Engineer partners closely with Software Engineering, Product, Architecture, Data, Security, and Operations, ensuring AI solutions are high-quality, cost-efficient, reliable, and compliant with organizational policies.

Responsibilities

AI Strategy & Architecture

• Define the multi-year AI technical strategy and roadmap aligned to business priorities.
• Architect large-scale AI and machine learning solutions across multiple platforms and products.
• Design LLM applications using retrieval augmentation, prompt engineering, structured outputs, tool/function calling, and agent-based workflows.
• Establish patterns for agentic automation, including task decomposition, routing, memory/state management, and human review checkpoints.
• Identify emerging AI technologies and lead proofs of concept through to scalable implementation.
• Determine the right solution approach for each problem — prompting, retrieval, fine-tuning, deterministic logic, or conventional software.

AI Platform & Foundations

• Own the enterprise AI reference architecture, platform strategy, and engineering standards.
• Evaluate commercial models, platforms, and vendor tools; make build-versus-buy recommendations.
• Guide technical relationships with cloud providers and AI vendors.
• Anticipate scaling, reliability, and cost challenges and drive architectural investments to address them.
• Set organization-wide targets for accuracy, groundedness, latency, cost, and reliability.
• Define evaluation strategies, benchmark datasets, and automated testing frameworks.
• Optimize retrieval pipelines, embeddings, chunking strategies, and inference performance.
• Lead error analysis and human-in-the-loop review programs.
• Establish quality gates and regression thresholds for all AI releases.
• Set technical direction for pipelines preparing structured and unstructured data for AI workloads.
• Partner with Data Engineering to define data contracts, feature sets, vector stores, and lineage.
• Define standards for data minimization, masking, and secure handling of sensitive information.

MLOps & Production Support

• Define CI/CD, versioning, and infrastructure standards for AI workloads.
• Establish promotion and rollback practices for prompts, agents, and model configurations.Set observability strategies for drift, accuracy, token usage, latency, cost, and reliability.
• Own model and inference spend strategy, including caching, routing, and model right-sizing.
• Serve as the final escalation point for critical production issues in AI-enabled features.

Responsible AI, Security & Compliance

• Lead responsible AI practices and ensure solutions meet security, privacy, and regulatory requirements.
• Partner with Security and Compliance to mitigate AI-specific risks such as prompt injection and data leakage.
• Define guardrails, content filtering, human oversight, and audit trails for high-impact use cases.
• Track emerging AI regulations and translate them into engineering policy.

Technical Leadership & Enablement

• Advise executive leadership on AI strategy, risk, investment, and capability.
• Influence architecture and engineering direction across the organization.
• Mentor and develop senior engineers and raise AI fluency across teams.
• Lead cross-functional initiatives and resolve complex technical disagreements.
• Communicate AI capabilities, limitations, and risks to both technical and non-technical audiences.
• Contribute to hiring strategy and interviewing for AI-related roles.
• Advance agentic development practices and specification-driven workflows.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience; Master’s preferred.
• 10+ years of software engineering experience, including 6+ years building production AI/ML solutions.
• Proven experience delivering enterprise-scale AI systems with measurable business impact.
• Expert programming skills in Python and experience with enterprise languages such as C#, Java, or similar.
• Deep hands-on expertise with LLMs, generative AI, retrieval augmentation, embeddings, vector search, structured outputs, and model evaluation.
• Extensive experience operating AI workloads on cloud platforms (AWS, Azure, or equivalent).
• Strong knowledge of APIs, microservices, distributed systems, SQL, Git, CI/CD, containerization, and automated testing.
• Demonstrated enterprise-level technical leadership and ability to set technical strategy.
• Experience with agentic coding tools (e.g., GitHub Copilot, Claude Code, Cursor) and multi-step agent workflows.
• Practical understanding of LLM behavior in production, including hallucination, latency, and cost tradeoffs.
• Exceptional communication skills with the ability to influence executives and engineers.

Preferred Qualifications

• Master’s or PhD in a relevant technical field.
• Experience with intelligent document processing, OCR, or unstructured data extraction.
• Background in applied machine learning, experiment design, and error analysis.
• Experience with Docker, Kubernetes, or infrastructure-as-code.
• Familiarity with agent frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, etc.).
• Experience with MLOps tooling, model registries, feature stores, or LLM observability platforms.
• Experience with fine-tuning or task-specific models.
• Familiarity with responsible AI frameworks and AI security best practices.
• Prior experience establishing an AI function or serving as the senior AI technical leader for an organization.