Applied AI Engineering Lead

N2P Systems is seeking an experienced Applied AI Engineering Lead to join our enterprise technology delivery team in Nashville, TN / Dallas, TX / Tampa, FL, USA. At N2P Systems, you will put 5-8 years of hands-on experience with AI/ML Application Development, RAG Pipelines and Prompt Engineering to work on mission-critical enterprise systems. This is an existing contract, hybrid vacancy with N2P Systems.

N2P Systems is seeking an experienced Applied AI Engineering Lead to drive hands-on engineering across multiple high-visibility software and AI initiatives. This role combines application architecture, advanced software development, and practical AI engineering to deliver scalable, maintainable solutions that generate measurable customer and business value.

You will take an active role throughout the engineering lifecycle, from requirements analysis and architecture through implementation, testing, integration, and ongoing support. Alongside your technical contributions, you will mentor engineers, establish engineering standards, and help teams adopt AI-assisted and agentic development practices to improve delivery speed, consistency, and quality.

This is a hybrid position requiring a minimum of three days per week in a local office. Candidates must be within commuting distance of Nashville, Tennessee; Dallas, Texas; or Tampa, Florida.

N2P Systems uses AI to help screen and assess applications for this role. N2P Systems is an enterprise technology consulting and digital solutions firm operating across Canada, the United States and India.

Key Responsibilities:

• Drive engineering accountability for customer outcomes and business value through lean, high-quality technical implementations.

• Advocate for technical integrity, ensuring solutions remain feasible and aligned with product and business objectives.

• Lead requirements analysis, low-level architecture, component design, development, testing, integrations, and ongoing support.

• Maintain architectural consistency and technical stack integrity in accordance with enterprise engineering standards.

• Oversee dependencies, implementation quality, data management, code design, maintenance, and operational considerations.

• Remain hands-on with software development while continuously adopting new programming languages, frameworks, and AI-enabled engineering practices.

• Produce technical specifications and scalable, maintainable code while conducting code reviews and mentoring fellow engineers.

• Develop rapid, cost-effective experiments and AI-assisted prototypes to validate technical approaches and address customer needs.

• Collaborate directly with customers and product teams throughout the delivery lifecycle to validate requirements and technical outcomes.

• Work effectively in uncertain environments, prioritizing evidence-based decisions and practical implementation over excessive planning.

• Partner with product management, experience, delivery, and engineering teams to develop effective technical solutions.

• Apply AI and agentic practices across the Secure Software Development Lifecycle, incorporating automation and quality checks into engineering workflows.

• Use AI-assisted tools for code generation, code reviews, testing, and technical documentation while maintaining engineering standards.

• Build domain knowledge quickly and translate business and user requirements into technical specifications and working software.

• Contribute to engineering communities, strengthen cross-functional relationships, and support collaborative technical decision-making.

Required Skills & Qualifications:

• Bachelor's degree in Computer Science, Software Engineering, Data Science, Machine Learning, or a related discipline.

• 6–8 years of overall software engineering experience, with practical exposure to modern programming languages, frameworks, and application development technologies.

• At least 2 years of hands-on experience building AI/ML applications.

• At least 2 years of practical Generative AI experience involving LLM integration, RAG pipelines, prompt engineering, and vector databases.

• At least 2 years of cloud-native engineering experience involving FaaS, PaaS, or microservices on a major cloud platform.

• Experience working with cloud-based AI/ML services and integrating AI capabilities into enterprise applications.

• Understanding of software engineering fundamentals, including object-oriented programming and design, data structures, algorithms, and code instrumentation.

• Experience contributing to engineering standards, mentoring engineers, and continuously improving development practices.

• Familiarity with technical architecture documentation, including business context, sequence, activity, state, entity relationship, and data flow diagrams.

• Understanding of AI-augmented specification-driven development and modern engineering workflows.

• Experience with engineering methodologies and tools supporting quality, security, reliability, and rapid software delivery.

• Ability to work independently, lead technical workstreams, and collaborate effectively with cross-functional teams.

• Strong written and verbal communication skills, with the ability to explain complex technical concepts and support evidence-based decisions.

• Strong attention to detail, professional relationship-building, prioritization, and deadline management.

• Ability to mentor engineers and provide clear technical guidance.

• Willingness to travel approximately 0–10%, depending on business and project requirements.

Preferred Qualifications:

• Hands-on experience with modern application technologies, including Angular, React, Node.js, Python, C#, .NET, Java, SQL, and NoSQL.

• Familiarity with machine learning frameworks such as PyTorch and TensorFlow.

• Experience with AI development frameworks such as LangChain and LangGraph.

• Exposure to OpenAI, Anthropic, and open-source LLM integrations.

• Experience with Azure, AWS, or GCP cloud platforms and services such as Azure OpenAI, AWS Bedrock, and Vertex AI.

• Knowledge of unit testing frameworks and automated quality assurance practices.

• Familiarity with XP, Lean engineering, DevSecOps, and Site Reliability Engineering practices.

• Experience with Azure DevOps, GitHub, SonarQube, and MLflow.

• Familiarity with agentic AI frameworks such as LangFuse, LangSmith, or equivalent multi-agent orchestration tools.

• Experience implementing AI-assisted code generation, automated testing, code review, and documentation workflows.

• Knowledge of AI-augmented specification-driven development and agentic Secure Software Development Lifecycle practices.