Applied AI Engineer

Role Overview

Key Responsibilities

• Contribute to the development of engineering solutions addressing complex product and business requirements.
• Participate in technical design, software development, testing, system integrations, and ongoing application support.
• Assist with requirements analysis and component-level architectural decisions alongside senior engineers.
• Maintain software quality through dependency management, code reviews, technical maintenance, and adherence to established engineering standards.
• Develop scalable, maintainable, and supportable application code using modern programming languages and frameworks.
• Experiment with AI-generated prototypes and emerging engineering approaches to validate solutions efficiently.
• Collaborate with product, experience, and delivery teams throughout the development and deployment lifecycle.
• Incorporate AI and agentic SSDLC practices into engineering workflows, including automated deployments and integrated quality checks.
• Leverage AI tools for code generation, code reviews, testing, and technical documentation.
• Translate business and user requirements into technical specifications and working software implementations.
• Continuously explore new technologies, frameworks, programming languages, and AI-enabled development practices.
• Contribute to technical discussions, communicate implementation challenges, and share relevant engineering trade-offs with the team.

Required Skills & Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Data Science, Machine Learning, or a related technical discipline.
• 3–5 years of overall software engineering experience with one or more relevant programming languages, frameworks, or technologies.
• At least 1 year of practical experience developing AI/ML applications.
• Hands-on exposure to Generative AI concepts, including LLM integration, Retrieval-Augmented Generation (RAG), prompt engineering, or vector database technologies.
• At least 1 year of cloud-native engineering experience involving FaaS, PaaS, or microservices on a major cloud platform.
• Exposure to cloud-based AI/ML services and technologies.
• Understanding of software engineering fundamentals, including object-oriented programming and design, data structures, algorithms, and technical architecture diagrams.
• Familiarity with engineering standards and established software development practices.
• Willingness to work independently while contributing effectively to collaborative engineering initiatives.
• Ability to work from one of the designated local offices at least 3 days per week.
• Willingness to travel approximately 0–10%, depending on project and business requirements.

Preferred Qualifications

• Experience with modern frontend and backend technologies, including Angular, React, Node.js, Python, C#, .NET, or Java.
• Familiarity with SQL/NoSQL databases, PyTorch, TensorFlow, LangChain, LangGraph, and unit testing frameworks.
• Exposure to LLM providers and platforms such as OpenAI, Anthropic, and open-source models.
• Familiarity with Azure OpenAI, AWS Bedrock, Vertex AI, or equivalent cloud-based AI/ML services.
• Experience with Agile methodologies, DevSecOps practices, Azure DevOps, GitHub, and SonarQube.
• Exposure to MLflow and agentic AI observability or development frameworks such as LangFuse and LangSmith.
• Practical familiarity with Business Context Diagrams, sequence diagrams, activity diagrams, state diagrams, entity relationship diagrams, and data flow diagrams.
• Experience incorporating AI tools into software development, code reviews, automated testing, and documentation workflows.
• Adaptability, attention to implementation quality, and enthusiasm for learning through mentorship and practical engineering experience.

Applicants will be asked to answer the following qualifying questions when applying for this position:

• 1Are you legally authorized to work in the United States without requiring visa sponsorship?
• 2Do you have at least 1 year of hands-on experience building AI/ML applications, including practical experience with at least one of the following: LLM integration, RAG pipelines, prompt engineering, or vector databases?
• 3Do you have at least 1 year of cloud-native engineering experience using FaaS, PaaS, or microservices on Azure, AWS, or GCP, including exposure to cloud AI/ML services?
• 4Can you reliably commute to a local office in Nashville, TN, Dallas, TX, or Tampa, FL, and work on-site at least 3 days per week?