Senior AI Engineer, Voice & Agentic Systems

FIS Global•Jacksonville, FL

10h

About The Position

We are seeking a highly technical Senior AI Engineer to lead the development of our next-generation banking customer experience by building real-time, conversational Agentic AI voice systems from the ground up. Above all else, your primary skill must be an insatiable curiosity to learn, deconstruct, and implement emerging systems. The generative AI landscape evolves weekly, and this role requires a relentless drive to experiment with novel architecture, ingest new research, and translate bleeding-edge concepts into production-grade banking solutions. This role will focus on building production-grade, multi-agent solutions that combine deterministic workflow control with LLM-driven reasoning, real-time speech interaction, secure API integration, and measurable business outcomes. The ideal candidate brings deep software engineering expertise, strong curiosity for emerging AI architectures, and the ability to translate rapidly evolving agentic AI capabilities into reliable, compliant, and scalable financial technology solutions.

Requirements

5+ years of backend or platform engineering experience, with strong proficiency in Python, Java, C#, or .NET and a solid foundation in data structures, algorithms, APIs, and distributed systems.
Hands-on experience designing or building LLM-powered applications, agentic workflows, tool/function calling, state management, retrieval patterns, and multi-step task orchestration.
Practical experience with conversational AI, speech-to-text, text-to-speech, NLP/NLU, real-time voice interaction patterns, or voice assistant platforms.
Experience moving AI solutions from prototype to production with attention to latency, reliability, scalability, observability, cost efficiency, and operational resilience.
Experience defining evaluation methods for AI systems, including prompt testing, response quality, hallucination risk, tool-call accuracy, routing performance, A/B testing, and regression validation.
Experience deploying services on Azure, AWS, or similar cloud platforms using containers, serverless architecture, CI/CD pipelines, infrastructure as code, and secure configuration practices.
Ability to design solutions that protect sensitive data and align with privacy, security, Responsible AI, and regulated-industry expectations.
Strong debugging, root-cause analysis, and production support skills across complex, asynchronous, distributed, and non-deterministic systems.
Demonstrated curiosity and ability to rapidly evaluate emerging AI frameworks, models, research patterns, and architecture approaches for practical enterprise use.

Nice To Haves

Experience delivering AI, cloud, or customer engagement solutions in banking, fintech, or other regulated industries.
Experience with contact center modernization, IVR transformation, CCaaS platforms, or enterprise customer experience systems.
Experience taking emerging AI concepts from research or prototype into reliable, secure, and scalable production implementations.
Advanced degree in Computer Science, Engineering, Artificial Intelligence, or a related field, or equivalent practical experience.

Responsibilities

Architect and deliver enterprise-scale Agentic voice AI systems that combine deterministic workflow control, LLM-driven reasoning, tool orchestration, memory management, and secure API integration across regulated banking use cases.
Build production-grade Agentic AI solutions that integrate speech-to-text, text-to-speech, LLMs, cognitive services, and backend enterprise systems to deliver natural, reliable, real-time customer interactions.
Design agentic workflows that enable AI systems to complete complex, multi-step tasks using structured planning, function calling, tool routing, state management, and human-in-the-loop escalation where appropriate.
Optimize real-time voice interactions for low latency, reliability, scalability, and cost efficiency through streaming architecture, token optimization, semantic caching, prompt tuning, and performance engineering.
Deploy highly available AI services using cloud-native architectures across Azure and AWS, including Kubernetes, container platforms, serverless services, multi-region resilience, and secure DevOps practices.
Build evaluation, experimentation, and observability frameworks to measure conversational quality, routing accuracy, latency, containment, hallucination risk, tool-call reliability, and production performance.
Implement security, privacy, Responsible AI, and compliance guardrails to protect sensitive customer data and ensure AI systems operate within financial-services regulatory expectations.
Troubleshoot complex issues across distributed AI systems, including network latency, model behavior, prompt failures, token-limit breaches, API errors, orchestration defects, and non-deterministic agent outcomes.
Collaborate with product, architecture, engineering, cloud, data, security, and business teams to translate business requirements into scalable AI solutions.
Mentor engineers in AI-first software engineering practices, including reusable component design, automated testing, defensive programming, observability, deployment discipline, and production support.