Principal AI Engineer

Burtch Works; New York (NY); Hybrid; USD 180,000 - 260,000; Full time

Job summary

Burtch Works seeks a Principal AI Engineer to lead the design, build, and delivery of enterprise AI solutions in a hybrid New York setting. You will work with business stakeholders, product managers, data scientists, software engineers, and tech partners to deploy scalable, secure, and responsible AI products.

You will mentor engineers, shape AI engineering standards, and drive value from AI investments while staying current with emerging technologies and enterprise governance.

Qualifications

• Advanced degree in Computer Science, AI, Engineering, Mathematics, or related field.
• 8+ years delivering ML/AI solutions with technical leadership.
• Expertise in production AI applications spanning ML, Generative AI, and agentic AI architectures.
• Strong software engineering in Python; working knowledge of SQL and JavaScript/TypeScript; experience with CI/CD, Docker, Kubernetes.
• Deep hands-on with LLMs, prompt engineering, RAG, and production retrieval architectures.
• Experience deploying AI solutions on Google Cloud Vertex AI and familiarity with AWS AI services and Databricks.

Responsibilities

• Lead end-to-end delivery of AI and Generative AI solutions across enterprise contexts.
• Design, deploy, and operate production AI applications on the enterprise AI platform.
• Architect multi‑agent AI systems with modern orchestration frameworks.
• Evaluate and integrate AI technologies across the solution stack for scalability and security.
• Establish engineering best practices for AI development, testing, observability, and governance.
• Prototype AI-enabled applications and experiences to validate concepts and drive adoption.
• Provide technical leadership and mentorship to AI engineers and data scientists.
• Partner with business leaders to define success metrics and optimize AI value.

Skills

Python programming

SQL

JavaScript/TypeScript

CI/CD

Docker

Kubernetes

Large language models

RAG

Multi-agent AI

Mentoring

Communication

Education

Master's or PhD in CS/AI/Engineering

Tools

LangGraph

Google Cloud Vertex AI

AWS Bedrock

SageMaker

Databricks

Codex/Claude Code

Job description

Job Title:

Principal AI Engineer

Location:

New York, NY (Hybrid – 3 Days In Office)

About The Role

This is a hybrid role requiring 3 days per week in office in New York, NY. Limited travel of less than 10% may be required for vendor meetings, industry conferences, and team collaboration.

Job Summary

As a Principal AI Engineer within the Artificial Intelligence & Data organization, you will serve as a senior technical leader responsible for designing, building, and delivering enterprise AI solutions that create measurable business value. This is a hands‑on engineering role spanning traditional machine learning, Generative AI, and Agentic AI, requiring deep technical expertise across the full AI solution lifecycle from opportunity assessment and solution architecture through deployment, monitoring, and continuous improvement. Working closely with business stakeholders, product managers, data scientists, software engineers, and technology partners, you will lead the delivery of production‑ready AI solutions on the enterprise AI platform, leveraging enterprise capabilities to rapidly deliver scalable, secure, and responsible AI applications that solve complex business problems. In addition to leading technical delivery, you will help define AI engineering standards, influence enterprise AI strategy, mentor engineers, and partner with the business to measure and maximize the value generated by AI investments.

Key Responsibilities

• Lead the end‑to‑end delivery of AI and Generative AI solutions, partnering with business, product, technology, and data teams to identify opportunities, shape solution strategies, and deliver measurable business outcomes.
• Design, develop, deploy, and operate production AI applications using the enterprise AI platform, leveraging existing CI/CD pipelines, infrastructure templates, security controls, and platform services.
• Architect and implement advanced AI solutions including agentic AI systems, multi‑agent orchestration, retrieval‑augmented generation (RAG), hybrid retrieval strategies, and human‑in‑the‑loop workflows to address complex business challenges.
• Evaluate, select, and integrate AI technologies across the solution stack, including foundation models, orchestration frameworks, cloud AI services, and modern software engineering tools, ensuring solutions are scalable, secure, and maintainable.
• Establish engineering best practices for AI development, including evaluation frameworks, automated testing, observability, responsible AI, safety validation, and production monitoring.
• Rapidly prototype AI‑enabled applications and user experiences to validate concepts, accelerate adoption, and inform product direction using modern development frameworks.
• Leverage AI‑assisted software development tools to accelerate engineering productivity while maintaining high standards for code quality, testing, and governance; develop internal engineering agents that improve software delivery through automation of testing, code review, deployment validation, and other engineering workflows.
• Provide technical leadership, mentorship, and architectural guidance to AI engineers and data scientists, fostering a culture of innovation, experimentation, technical excellence, and continuous learning.
• Partner with business leaders to define success metrics, measure solution performance, and continuously optimize AI solutions to maximize business value.
• Stay current on emerging AI technologies, evaluate their applicability to enterprise use cases, and influence the organization’s AI roadmap and engineering practices.

Requirements

• Master's or PhD in Computer Science, Artificial Intelligence, Engineering, Mathematics, or a related quantitative discipline.
• 8+ years of experience delivering machine learning and AI solutions that solve complex business problems, with increasing technical leadership and ownership.
• Demonstrated expertise designing and delivering production AI applications spanning machine learning, Generative AI, and agentic AI architectures.
• Strong software engineering skills in Python, with working knowledge of SQL and JavaScript or TypeScript; experience building, testing, deploying, and supporting production software using modern engineering practices including CI/CD, code review, Docker, and Kubernetes.
• Deep hands‑on experience with large language models, prompt engineering, retrieval‑augmented generation (RAG), hybrid retrieval techniques, knowledge graphs, and production retrieval architectures.
• Experience designing and implementing multi‑agent AI systems using modern orchestration frameworks (e.g., LangGraph, Google ADK, CrewAI) and tool integration protocols such as MCP or equivalent approaches.
• Experience evaluating AI systems using automated evaluation pipelines, safety testing, red teaming, hallucination detection, performance monitoring, and responsible AI practices.
• Experience deploying AI solutions on enterprise cloud platforms, particularly Google Cloud Vertex AI, with familiarity across AWS AI services (Bedrock, SageMaker) and modern data platforms including Databricks.
• Experience using AI‑assisted software engineering tools (e.g., Codex, Claude Code) to improve developer productivity, including developing engineering agents that automate software delivery workflows.
• Excellent communication, stakeholder management, and collaboration skills with the ability to influence technical direction and communicate effectively with both technical and business audiences.
• Demonstrated ability to mentor engineers, lead technical initiatives, and drive engineering excellence across teams.

Preferred Qualifications

• Experience within life insurance, financial services, or other highly regulated industries.
• Experience defining enterprise AI architecture, engineering standards, or AI governance frameworks.
• Experience leading cross‑functional AI transformation initiatives across multiple business domains.
• Contributions to the AI community through publications, open‑source projects, conference presentations, or technical thought leadership.