AI Engineer

SWBC•San Antonio, TX

About The Position

SWBC is seeking a talented individual to join our growing AI team as an AI Engineer. This role is responsible for designing, developing, and deploying AI-powered solutions that drive operational improvements and business outcomes across the organization. The ideal candidate will have hands-on experience with agentic AI systems, large language models (LLMs), prompt engineering, and production-ready AI applications within a security-conscious and regulated environment. Why you'll love this role: As an AI Engineer, you will play a key role in helping SWBC accelerate its AI initiatives by building and deploying intelligent solutions that solve real business challenges. You'll work with emerging technologies, including agentic AI frameworks, generative AI models, and AWS AI services, while partnering with business and technology teams to deliver measurable outcomes. This is an opportunity to work in a fast-moving environment where ideas quickly move from concept to production. If you enjoy building, experimenting, and implementing AI solutions that create operational efficiencies and business value, this role offers the chance to make a meaningful impact while helping shape SWBC's AI capabilities.

Requirements

Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.
Minimum of three (3) years of experience developing AI, machine learning, or generative AI solutions in a professional environment.
Hands-on experience developing and deploying solutions using Python.
Experience building and implementing agentic AI systems and AI-powered workflows.
Working knowledge of large language models (LLMs), foundation models, prompt engineering, and generative AI concepts.
Experience leveraging both pre-trained models and custom-trained AI models.
Experience developing APIs and integrating AI services into enterprise applications.
Understanding of vector databases, retrieval-augmented generation (RAG), fine-tuning approaches, and model deployment practices.
Experience working within AWS cloud environments and AWS AI services.
Knowledge of data pipelines and how they support AI and machine learning workflows.
Experience operating within a regulated environment with an understanding of security, governance, and Responsible AI principles.
Strong problem-solving skills with the ability to execute in a fast-paced environment and deliver solutions quickly.
Strong communication and collaboration skills with the ability to work effectively across business and technical teams.

Nice To Haves

AWS Certified Machine Learning Engineer – Associate certification.
AWS Certified Generative AI Developer - Professional
Experience training, fine-tuning, or hosting open-source large language models internally.
Experience with AWS Bedrock and Bedrock AgentCore.
Experience with LangChain, LangGraph, and Strands frameworks.
Familiarity with Model Context Protocol (MCP) and agent-to-agent communication protocols.
Experience with C# development.
Experience supporting AI solutions through production deployments and ongoing optimization.
Exposure to MLOps practices and AI operationalization.

Responsibilities

Design, develop, and deploy AI-powered applications and agentic AI solutions that support business and operational objectives.
Build, test, and optimize AI workflows utilizing foundation models, open-source LLMs, and internally trained models.
Develop and maintain AI agents, APIs, and supporting services that enable scalable and secure AI operations.
Implement retrieval-augmented generation (RAG), vector database integrations, prompt engineering techniques, and agent-to-agent communication patterns.
Utilize frameworks such as LangChain, LangGraph, Strands, and AWS Bedrock AgentCore to develop enterprise AI solutions.
Deploy AI and machine learning models into production environments while ensuring reliability, scalability, and performance.
Partner with cross-functional stakeholders to evaluate business use cases and translate requirements into AI-driven solutions.
Develop and support data pipelines that enable model training, inference, and AI application performance.
Ensure AI solutions adhere to Responsible AI principles, security standards, governance requirements, and regulatory compliance expectations.
Contribute to the evolution of SWBC's AI architecture as the organization continues to expand and mature its AI platform capabilities.
Document solution architecture, workflows, models, and technical processes to support operational excellence and knowledge sharing.
Continuously evaluate emerging AI technologies, models, and frameworks to identify opportunities for innovation and business improvement.