Senior AI Engineer – Generative AI & Applied Innovation (Financial Services)

Cetera Financial Group•Dallas, TX

1d•$140,000 - $185,000•Hybrid

About The Position

We are at the forefront of transforming the future of technology in the financial industry, and we seek curious, practical individuals to help us pave the way. Our team is not intimidated by taking calculated risks, as they relish a good challenge and are eager to engage in problem-solving. As a member of our team, you will work alongside like-minded experts in a culture that is deeply rooted in innovation and progression. Join us to be part of a transformative journey that can shape the industry's future. Introduction: We are seeking a hands-on Senior AI Architect-Engineer who bridges enterprise architecture leadership with deep engineering execution. You will define and evolve our Generative AI reference architectures while building high-quality solutions end-to-end—accelerating delivery of RAG systems, copilots, and agentic workflows that enhance client experiences, streamline operations, and unlock business value across financial services. This role balances strategy and governance with pragmatic coding, prototyping, and productionizing AI systems. Locations: Dallas Texas (strongly preferred) or El Segundo, CA What would you do: A tenured, self-driven AI leader who combines systems thinking and architectural rigor with hands-on engineering. You proactively learn and apply emerging GenAI tools, collaborate across Business, Risk, and Technology, and mentor engineers while aligning delivery to enterprise guardrails.

Requirements

12 years of professional experience in Architectural Technology
3+ years of experience in Artificial Intelligence (AI).
Hands-on experience architecting and building Generative AI systems, including LLM orchestration, RAG pipelines, and copilots.
Experience with leading AI platforms and frameworks: Azure OpenAI, OpenAI, Hugging Face, LangChain, and agent frameworks
Strong software engineering skills in Python, TypeScript, and Java, with cloud-native development on Azure and AWS.
Proven ability to design and deploy production ML systems: feature stores, model registries, CI/CD pipelines, observability, and data privacy controls.
Expertise in retrieval systems: semantic chunking, metadata extraction, hybrid search, and vector stores (Azure AI Search, Pinecone).
Deep understanding of security and compliance patterns: SSO, RBAC, secrets management, multi-tenant isolation; familiar with financial services risk and controls.
Skilled in prompt engineering and evaluation, including prompt versioning, catalogs, and test harnesses.
Proficient in IaC and automated delivery pipelines (Terraform, Bicep, GitHub Actions, Azure DevOps).
Strong cross-functional leadership : driving architecture reviews, establishing standards, mentoring engineers, and collaborating with Risk/Compliance teams.
Excellent communicator of trade-offs, cost/performance, and ROI to both technical and business stakeholders.

Nice To Haves

Examples of enterprise-grade GenAI reference implementations reused across multiple use cases.
Proof of reducing time-to-production via standardized RAG patterns and shared components.
Demonstrated Responsible AI governance embedded in delivery workflows (evaluations, guardrails, and red teaming).
Architecting agentic solutions that integrate with existing systems (Salesforce, NICE, AWS) through robust APIs and observability.
Published internal architecture notes/patterns, mentorship and community contributions.
Deep understanding of business processes and how AI can be applied to improve them.
Pragmatic and collaborative, building trust across business and technical teams.
High integrity and accountability in architectural and technical decision-making.
Exceptional problem-solving skills and a bias for action.
Comfortable navigating ambiguity and creating structure where none exists .
Balance of architectural depth with practical execution and delivery.
Continuous learning mindset to stay current with AI/ML trends and applications.

Responsibilities

Own and evolve enterprise GenAI reference architectures (LLM orchestration, unified RAG patterns, retrieval, vector index, observability, security).
Design solution blueprints and integration patterns across data platforms, APIs, identity, secrets, and tenant isolation; ensure consistency via reusable components and IaC .
Lead architecture reviews and guardrail enforcement (Responsible AI, cost/performance, safety controls) while unblocking delivery teams.
Prototype quickly and build high-quality services: prompts, evaluators, retrievers, chunking pipelines, embeddings, and inference APIs.
Develop scalable ML/AI pipelines and microservices using cloud-native tools (e.g., Azure, AWS), with CI/CD and automated testing.
Implement agent frameworks and workflow orchestration (manual and autonomous) for business scenarios; catalog prompts and agents.
Partner with Data Engineering and MLOps to deploy, monitor, and lifecycle-manage models; establish model telemetry and cost/usage alerts.
Collaborate with AI Program Strategy, Product, Compliance, and LOB stakeholders to identify/prioritize use cases and align with operating model.
Contribute to internal libraries and shared components; publish patterns, design decisions, and best practices to the AI Community of Practice.
Drive build-vs-buy decisions, evaluate vendor capabilities, and ensure alignment with governance and ROI frameworks.