AI Engineer

Essential Duties Include

• 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.

Minimum Qualifications

• 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.

Preferred Skills

• 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.

Benefits

• Competitive overall compensation package
• Work/Life balance
• Employee engagement activities and recognition awards
• Years of Service awards
• Career enhancement and growth opportunities
• Ledership Academy and Mentor Program
• Continuing education and career certifications
• Variety of healthcare coverage options
• Traditional and Roth 401(k) retirement plans
• Lucrative Wellness Program