N
Applied AI Engineer
N2P Systems
4 hours ago
Full-time
On-site
Nashville, Tennessee, United States
We are seeking a hands-on
Applied AI Engineer
with 3–5 years of software engineering experience and practical experience building
AI/ML and Generative AI applications .
In this role, you will contribute to high-impact engineering initiatives, applying modern software development practices alongside
AI and agentic engineering tools
across the software development lifecycle. You’ll work closely with product, engineering, and cross-functional teams to design, develop, test, deploy, and support scalable solutions that deliver measurable business and customer value.
The ideal candidate is a strong software engineer who is curious about AI, comfortable learning new technologies, and excited to apply
GenAI, LLMs, RAG, prompt engineering, and AI-enabled development practices
to real-world engineering problems.
Key Responsibilities Design, develop, test, integrate, deploy, and support software components and AI-enabled applications. Participate in requirements analysis and component-level technical design. Build scalable, maintainable, and high-quality software using modern programming languages and frameworks. Apply
AI and Agentic SSDLC practices
across development, testing, deployment, and maintenance. Leverage AI tools for
code generation, code review, testing, debugging, documentation, and developer productivity . Develop and integrate
Generative AI/LLM solutions , including LLM APIs, RAG pipelines, prompt engineering, and vector-based retrieval. Contribute to rapid prototyping and experimentation, including AI-assisted prototypes and proof-of-concepts. Work with cloud-native architectures, microservices, FaaS/PaaS, and AI/ML services across
Azure, AWS, or GCP . Participate in code reviews and follow engineering standards for code quality, security, scalability, and maintainability. Collaborate with product management, engineering, experience, and delivery teams to translate business and user needs into technical solutions. Contribute to automated deployments and quality checks throughout the engineering lifecycle. Troubleshoot technical issues and support applications in production. Communicate technical decisions, progress, blockers, risks, and trade-offs clearly. Continuously learn and adopt emerging AI, software engineering, and agentic development practices.
Required Qualifications Bachelor’s degree in
Computer Science, Software Engineering, Data Science, Machine Learning , or a related technical discipline. 3–5 years of software engineering experience
with one or more of the following: Python Java C# / .NET Node.js React / Angular SQL / NoSQL PyTorch / TensorFlow LangChain / LangGraph Unit testing frameworks 1+ year of hands-on experience building AI/ML applications. Practical experience with
Generative AI / LLM technologies , including one or more of: OpenAI Anthropic Open-source LLMs RAG Prompt engineering Vector databases LLM application development 1+ year of cloud-native engineering experience
using FaaS, PaaS, microservices, or similar architectures on
Azure, AWS, or GCP . Exposure to cloud AI/ML services such as: Azure OpenAI AWS Bedrock Google Vertex AI Understanding of software engineering fundamentals, including: Object-Oriented Programming / Design Data structures and algorithms System and component design Data flow and entity relationship concepts Sequence, activity, and state diagrams Working knowledge of modern engineering standards and best practices. Ability to work effectively both independently and collaboratively. Strong written and verbal communication skills with attention to quality and detail.
Preferred Qualifications Experience with
Agile / DevSecOps
environments. Experience with
GitHub, Azure DevOps (ADO), SonarQube, or MLflow . Exposure to AI/agent observability and evaluation tools such as
LangSmith, LangFuse, or equivalent . Experience with AI-assisted software development and agentic engineering workflows. Experience building and deploying production-grade AI applications. Ability to quickly learn new technologies, frameworks, and engineering practices.
Applied AI Engineer
with 3–5 years of software engineering experience and practical experience building
AI/ML and Generative AI applications .
In this role, you will contribute to high-impact engineering initiatives, applying modern software development practices alongside
AI and agentic engineering tools
across the software development lifecycle. You’ll work closely with product, engineering, and cross-functional teams to design, develop, test, deploy, and support scalable solutions that deliver measurable business and customer value.
The ideal candidate is a strong software engineer who is curious about AI, comfortable learning new technologies, and excited to apply
GenAI, LLMs, RAG, prompt engineering, and AI-enabled development practices
to real-world engineering problems.
Key Responsibilities Design, develop, test, integrate, deploy, and support software components and AI-enabled applications. Participate in requirements analysis and component-level technical design. Build scalable, maintainable, and high-quality software using modern programming languages and frameworks. Apply
AI and Agentic SSDLC practices
across development, testing, deployment, and maintenance. Leverage AI tools for
code generation, code review, testing, debugging, documentation, and developer productivity . Develop and integrate
Generative AI/LLM solutions , including LLM APIs, RAG pipelines, prompt engineering, and vector-based retrieval. Contribute to rapid prototyping and experimentation, including AI-assisted prototypes and proof-of-concepts. Work with cloud-native architectures, microservices, FaaS/PaaS, and AI/ML services across
Azure, AWS, or GCP . Participate in code reviews and follow engineering standards for code quality, security, scalability, and maintainability. Collaborate with product management, engineering, experience, and delivery teams to translate business and user needs into technical solutions. Contribute to automated deployments and quality checks throughout the engineering lifecycle. Troubleshoot technical issues and support applications in production. Communicate technical decisions, progress, blockers, risks, and trade-offs clearly. Continuously learn and adopt emerging AI, software engineering, and agentic development practices.
Required Qualifications Bachelor’s degree in
Computer Science, Software Engineering, Data Science, Machine Learning , or a related technical discipline. 3–5 years of software engineering experience
with one or more of the following: Python Java C# / .NET Node.js React / Angular SQL / NoSQL PyTorch / TensorFlow LangChain / LangGraph Unit testing frameworks 1+ year of hands-on experience building AI/ML applications. Practical experience with
Generative AI / LLM technologies , including one or more of: OpenAI Anthropic Open-source LLMs RAG Prompt engineering Vector databases LLM application development 1+ year of cloud-native engineering experience
using FaaS, PaaS, microservices, or similar architectures on
Azure, AWS, or GCP . Exposure to cloud AI/ML services such as: Azure OpenAI AWS Bedrock Google Vertex AI Understanding of software engineering fundamentals, including: Object-Oriented Programming / Design Data structures and algorithms System and component design Data flow and entity relationship concepts Sequence, activity, and state diagrams Working knowledge of modern engineering standards and best practices. Ability to work effectively both independently and collaboratively. Strong written and verbal communication skills with attention to quality and detail.
Preferred Qualifications Experience with
Agile / DevSecOps
environments. Experience with
GitHub, Azure DevOps (ADO), SonarQube, or MLflow . Exposure to AI/agent observability and evaluation tools such as
LangSmith, LangFuse, or equivalent . Experience with AI-assisted software development and agentic engineering workflows. Experience building and deploying production-grade AI applications. Ability to quickly learn new technologies, frameworks, and engineering practices.