Principal Full Stack Software Engineer -GEN AI

Citizens•Phoenix, AZ

15h•Onsite

About The Position

Your role as Principal of Software Engineering is to work with engineering teams and architecture to produce high-quality technology solutions. You will be given the autonomy to lead, design, and develop innovative solutions to some of the biggest technical issues facing the banking industry. As Principal, you will serve as a peer-leader tasked with pursuing cutting-edge initiatives and solutions. The breadth of Citizens operations ensures a diversity of projects as the bank pivots towards innovation and customer experience. We are seeking a Principal Software Engineer with expertise in building Generative AI (GenAI) solutions and designing Agentic experiences integrated into the Software Development Life Cycle (SDLC). The ideal candidate will develop intelligent agents and AI-driven workflows that enhance automation, decision-making, and developer productivity across engineering platforms.

Requirements

7+ years of hands‑on software development, with proven experience in developing and supporting commercial software products sold to non‑technical customers in vertical markets.
Demonstrated ability to lead and mentor software engineers.
5+ years experience in querying, analyzing, and managing big data.
Mastery of multiple programming languages, including at least one front‑end framework (Angular/React/Vue), such as Python3, Java, JavaScript, Ruby, Golang, C, C++, etc.
Cloud experience (AWS/Azure/GCP), including managing sensitive assets.
Bash and Linux experience.
CI/CD pipeline experience (CircleCI, Jenkins, GitHub Actions, or equivalent).
Strong communication (both oral and written) and interpersonal skills.
Cloud certifications such as AWS Solutions Architect, AI/ML Certifications.
Understand data structure concepts such as linked lists, dictionaries, arrays, custom object creation, etc.
Hands‑on with LLM frameworks/services (e.g., OpenAI, AWS Sagemaker and Bedrock, Hugging Face, LangChain/LangGraph) and agent orchestration patterns.
Proficiency in RAG techniques, embedding models, and vector databases .
Familiarity with ML/LLM deployment, prompt engineering, fine‑tuning, and evaluation/guardrail techniques.
Experience with containerization and orchestration (Docker, Kubernetes) and integrating AI services into CI/CD.
Understanding of responsible AI, privacy, and security considerations for production AI systems.

Nice To Haves

3+ years of experience in the financial services industry, developing solutions for consumer banking, portfolio management, trading, compliance or wealth management
Understanding of banking system and custodial and consumer banking operations
Prior work embedding AI into developer platforms (e.g., code review automation, test generation, knowledge assistants).
Experience implementing observability/evaluation agents for monitoring AI workflow performance (quality, drift, latency, cost).
Exposure to model governance and risk management practices (e.g., model documentation, validation, monitoring, auditability).
Prior work embedding AI into developer platforms (e.g., code review automation, test generation, knowledge assistants).

Responsibilities

Participating in an environment rapidly transforming to the Agile methodology, adhering to best practices, and collaborating effectively with your teammates.
Collaborating and contributing insight to solution design ideation, ensuring both the success of the product and adherence to enterprise architecture principles.
Designing, modifying, developing, and implementing software solutions. Building modern, architecturally sound components, tools and applications to meet mission-driven strategic business goals.
Infusing quality of service characteristics, such as scalability, manageability, and maintainability, into distributed service-based framework to create or expand business or technical capabilities.
Employing industry best practices to evaluate, correct and prevent vulnerabilities during the software development process.
Serving as a peer-leader, encouraging a culture of innovation and accountability while adhering to Agile best practices.
Design and implement GenAI‑powered applications and agentic workflows embedded within SDLC tools and processes to enhance automation, decision‑making, and developer productivity.
Build and integrate RAG (Retrieval‑Augmented Generation) pipelines for enterprise‑scale AI solutions, including document ingestion, embeddings, and contextual retrieval.
Develop APIs, microservices, and integration layers that enable AI‑driven automation across engineering platforms and CI/CD pipelines.
Implement vector database solutions (e.g., Pinecone, Weaviate, Milvus) for semantic search and knowledge retrieval; optimize embedding strategies and latency.
Collaborate with product, architecture, security, and DevOps to embed AI capabilities into build/test/release workflows (e.g., gated PR checks, automated code reviews, test case generation).
Ensure compliance with security, privacy, and responsible AI standards, including model evaluation, guardrails, and human‑in‑the‑loop review.
Establish observability for AI systems (telemetry, tracing, evaluation harnesses) to measure accuracy, drift, latency, and cost; drive continuous improvement cycles.