Principal AI Platform Engineer

Team: IT

Location: US

Commitment: Full-time

Workplace Type: remote

Accountabilities:

• Design, develop, and support enterprise-scale Generative AI solutions for AI-assisted development, documentation, testing, analytics, workflow automation, and other business and technology use cases.
• Lead the architecture, implementation, optimization, and operational support of Retrieval-Augmented Generation solutions, including ingestion pipelines, embeddings, vector stores, retrieval frameworks, and semantic search capabilities.
• Design, review, and approve agent-based and tool-integrated AI architectures, including multi-step LLM workflows built with approved enterprise platforms and services.
• Develop and oversee APIs, shared services, and reusable frameworks that securely connect AI models with internal systems, enterprise platforms, and approved third-party tools.
• Establish prompt-engineering standards, evaluation methodologies, testing frameworks, and benchmarking practices to continuously improve AI solution quality, reliability, safety, and performance.
• Author secure, scalable, organized, and maintainable production code using multiple programming languages, including Java, Python, and C#, while promoting software engineering excellence across teams.
• Lead technical discussions with Product Managers, Architects, Engineers, and senior stakeholders to translate business objectives into scalable enterprise AI solutions.
• Serve as a subject matter expert in Generative AI, AI platform engineering, enterprise application development, and responsible AI practices.
• Mentor and coach engineers on software engineering principles, AI architecture patterns, algorithms, data structures, distributed systems, and enterprise platform design.
• Contribute to technical roadmaps that balance strategic AI initiatives, platform modernization, innovation, operational excellence, and technical debt reduction.
• Define and champion architecture standards, reusable design patterns, and implementation guidance for AI-enabled solutions across multiple technology domains.
• Analyze engineering, operational, and AI performance metrics to identify opportunities for process improvement, platform optimization, reliability, and increased adoption.
• Lead the implementation of resiliency, performance, observability, security, and reliability engineering practices across AI platforms and services.
• Drive responsible AI, model governance, security, compliance, privacy, and risk-management practices throughout the AI development lifecycle.
• Participate in enterprise architecture forums, engineering councils, and leadership committees, presenting complex technical concepts and influencing AI strategy and technology direction.
• Identify technology, security, compliance, and operational risks requiring escalation and ensure appropriate adherence to enterprise policies, controls, audit requirements, and regulatory standards.
• Promote a collaborative and inclusive engineering environment while contributing to broader technology initiatives and other related responsibilities as needed.

Requirements

• Associate’s degree with at least 9 years of systems analysis and/or application development experience, or Bachelor’s degree with at least 7 years of relevant experience; alternatively, a combination of education and experience totaling at least 11 years, including 7 years of systems analysis and/or application development experience.
• Expert proficiency in at least one relevant programming language and advanced proficiency in at least one additional language, with hands-on production experience in Java, Python, C#, or comparable modern enterprise technologies.
• Strong foundation in software engineering principles, algorithms, data structures, distributed systems, and scalable application architecture.
• Advanced knowledge of Generative AI, Large Language Models, prompt engineering, and AI application development, with experience delivering AI-enabled applications within enterprise SDLC, security, compliance, and governance frameworks.
• Proven experience designing and integrating RESTful APIs, microservices, reusable services, and API-driven architectures.
• Deep practical expertise in RAG architectures, embeddings, vector databases, retrieval frameworks, and semantic search technologies is highly desirable.
• Strong understanding of Transformer architectures, attention mechanisms, tokenization strategies, model evaluation, benchmarking, and AI application performance.
• Experience designing AI agents, tool-integrated workflows, and advanced LLM orchestration frameworks.
• Expertise with Microsoft Azure, Azure AI Services, OpenAI technologies, and cloud-native AI platforms is preferred.
• Experience implementing enterprise Generative AI solutions within financial services or another highly regulated environment is a strong advantage.
• Experience establishing enterprise AI governance, responsible AI practices, model risk management, security controls, privacy standards, and compliance frameworks.
• Proven ability to influence technical direction, engineering standards, architectural decisions, and technology adoption across multiple teams.
• Experience leading large-scale technical initiatives, platform modernization programs, enterprise capability rollouts, or complex cross-functional technology initiatives.
• Strong experience with CI/CD pipelines, DevOps tooling, automated testing, observability, platform reliability, and production operations.
• Ability to operate autonomously while coordinating complex technical initiatives across multiple teams and stakeholders.
• Advanced written and verbal communication skills, including the ability to explain complex AI and technology concepts to senior leaders and executive audiences.
• Demonstrated subject matter expertise across AI platform engineering, software architecture, and enterprise application development, combined with strong strategic thinking and technical judgment.

Benefits

• Annual salary range of $139,700–$232,900 USD, with the specific compensation determined by factors including knowledge, skills, experience, and geographic location.
• Fully remote-eligible position within the United States.
• Opportunity to shape enterprise-scale Generative AI capabilities and influence AI architecture and technology strategy.
• Exposure to advanced technologies including LLMs, RAG, AI agents, Azure AI, OpenAI technologies, cloud-native platforms, and enterprise AI governance.
• Opportunity to mentor engineers and influence engineering standards, reusable architecture patterns, and platform modernization initiatives.
• High-impact work at the intersection of AI innovation, software engineering, security, compliance, and enterprise technology.
• Collaborative environment involving senior technology leaders, architects, engineers, product teams, and business stakeholders.