Engineer IV - Applied AI

PODS•Clearwater, FL

About The Position

Responsible for designing, developing, implementing, and supporting AI-powered solutions that address business challenges and create operational efficiencies across the organization. Leverages existing large language models (LLMs), retrieval-augmented generation (RAG) architectures, agentic workflows, data platforms, and software engineering practices to develop scalable solutions that enhance business processes, customer experiences, and decision-making capabilities. Partners closely with business stakeholders, product teams, and technology teams to identify opportunities, translate business needs into technical solutions, and deliver production-ready AI applications and services.

Requirements

Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Information Technology, or related field required.
Minimum of 7 years of progressive software engineering, data engineering, artificial intelligence, machine learning, or related technology experience.
Minimum of 3 years of experience designing, building, and deploying AI-enabled solutions within production environments.
Experience developing applications using LLMs, AI APIs, prompt engineering, retrieval-augmented generation (RAG), and agentic workflow technologies.
Experience with Python development, APIs, integrations, and cloud-based technologies.
Experience working with Snowflake, SQL, data transformation, and enterprise data platforms.
Experience implementing and supporting AI applications using modern frameworks and orchestration tools.
Strong analytical, problem-solving, organizational, and communication skills.

Nice To Haves

Master's degree preferred.
Equivalent combination of education, training, and experience may be considered.
Experience leading technical initiatives and influencing cross-functional teams preferred.

Responsibilities

Design, develop, implement, and support AI-powered applications and services utilizing large language models (LLMs), agentic workflows, prompt engineering techniques, APIs, and retrieval-augmented generation (RAG) architectures.
Evaluate business requirements and translate complex or ambiguous business problems into scalable technical solutions.
Research, evaluate, prototype, and recommend AI technologies, tools, platforms, and vendors to support business objectives.
Design and develop conversational AI solutions, chatbots, virtual assistants, and intelligent workflow automation capabilities.
Develop and maintain integrations between AI solutions, enterprise applications, APIs, databases, and cloud data platforms.
Query, transform, structure, and manage data to support AI and machine learning solutions utilizing platforms such as Snowflake and related technologies.
Develop and maintain retrieval pipelines, semantic search capabilities, vector databases, and data services supporting AI-enabled solutions.
Ensure data quality, security, governance, and performance requirements are met within assigned solutions.
Deploy, monitor, maintain, and optimize AI solutions in production environments.
Monitor application performance, reliability, utilization, and operating costs and implement improvements as appropriate.
Troubleshoot application, integration, and data issues and provide timely resolution to support business operations.
Create and maintain technical documentation, solution architecture diagrams, standards, and support materials.
Partner with business stakeholders, product teams, and technology teams to identify opportunities for AI adoption and process automation.
Provide technical leadership and subject matter expertise related to applied AI technologies and emerging industry trends.
Support pilot programs, proofs of concept, and innovation initiatives that advance organizational capabilities.
Promote adoption of AI-enabled solutions through training, knowledge transfer, and stakeholder engagement.
Stay current with developments in artificial intelligence, machine learning, software engineering, and data technologies.
Continuously evaluate solution effectiveness and recommend enhancements to improve business value, quality, scalability, reliability, and user experience.
Participate in architecture reviews, code reviews, and development best practices to ensure high-quality solution delivery.