Principal AI Engineer

Principal AI Engineer

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.

Model Development & Evaluation

• 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.

Data Engineering & Integration

• 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 softw…