Applied AI Engineer

N2P Systems; Nashville (TN); Hybrid; USD 110,000 - 165,000; Full time

Job summary

N2P Systems is seeking a hands-on Applied AI Engineer with 3–5 years of software engineering experience to build AI/ML and Generative AI applications. You will apply GenAI, LLMs, RAG, prompt engineering, and agentic development practices across the SDLC to deliver scalable solutions that meet business needs.

You will collaborate with product, engineering, and cross-functional teams to design, develop, test, deploy, and support AI-enabled software that adds measurable value to customers and the

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: Python, Java, C# / .NET, Node.js, React / Angular, SQL / NoSQL, PyTorch / TensorFlow.
• Hands-on experience building AI/ML applications and Generative AI technologies (OpenAI, RAG, LLMs).
• Cloud-native engineering experience on Azure, AWS, or GCP; familiarity with AI/ML services.

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 proofs-of-concepts.
• Work with cloud-native architectures, microservices, FaaS/PaaS, and AI/ML services across Azure, AWS, or GCP.
• Collaborate with product management, engineering, experience, and delivery teams to translate business needs into technical solutions.
• Contribute to automated deployments and quality checks throughout the engineering lifecycle.

Skills

Python

Java

C# / .NET

Node.js

React / Angular

SQL / NoSQL

PyTorch / TensorFlow

LangChain / LangGraph

Unit testing frameworks

OpenAI / LLMs

Cloud-native engineering

Azure / AWS / GCP

API design

Code generation / review tools

DevSecOps / Agile

Education

Bachelor’s degree in Computer Science, Software Engineering, Data Science, Machine Learning

Tools

GitHub

Azure DevOps (ADO)

SonarQube

MLflow

Job description

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
• Open-source LLMs
• RAG
• 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:
• AWS Bedrock
• 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.