Principal AI Platform Engineer

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.