AI Application Developer

Lenovo•Morrisville, NC

1d•Onsite

About The Position

We are seeking an AI Application Developer to join our U.S.-based AI Application Solutions team. You will design and develop AI-driven software solutions that translate machine-learning models, predictive analytics, anomaly detection, and large-language-model reasoning into scalable, reliable backend services and applications.

Requirements

Bachelor’s degree in Software Engineering, Computer Science, Artificial Intelligence, or a related field.
5+ year of experience developing software applications using C#, JavaScript, Python, or similar technologies.

Nice To Haves

Prior experience collaborating with a global team
Strong verbal, written, and spoken communication skills

Responsibilities

Design and develop AI-driven software solutions that translate machine-learning models, predictive analytics, anomaly detection, and large-language-model reasoning into scalable, reliable backend services and applications.
Build, test, and maintain enterprise-grade applications using Python, leveraging frameworks such as FastAPI, Flask, or Django, with a focus on clean architecture, modular design, and maintainability.
Develop and integrate machine-learning pipelines for training, evaluation, deployment, and monitoring of models using libraries such as scikit-learn, TensorFlow, PyTorch, or similar.
Implement LLM-powered capabilities including semantic search, intelligent assistants, automated summarization, and recommendation systems using modern AI frameworks and APIs.
Design and optimize data processing workflows for structured and unstructured data, ensuring performance, scalability, and reliability in high-volume production environments.
Collaborate closely with data scientists, AI/ML engineers, and frontend teams to align model outputs with application logic, APIs, and user-facing requirements.
Build and maintain APIs and services that expose AI and ML functionality securely and efficiently to internal and external consumers.
Implement logging, monitoring, and analytics to track model performance, system health, feature adoption, and workflow bottlenecks.
Ensure best practices in software engineering, including unit testing, integration testing, CI/CD pipelines, version control, and code reviews.
Optimize system performance, reliability, and scalability across cloud or hybrid environments.
Translate business and operational requirements into robust technical solutions that improve automation, decision-making, and overall system intelligence.
Own the end-to-end functionality of AI-enabled software components, from development through deployment and ongoing optimization.