Full Stack Developer (JavaScript, NodeJS, AI/ML Integration)

About the position

pureIntegration is seeking a Full Stack Developer to join an internal team, developing one of our growing service offerings. In this role you will assist in the development of both front and back-end systems working to integrate AI and machine learning technologies. The ideal candidate will have worked in a similar environment and possess strong UI and front-end skills while also having a solid background in back-end infrastructure and systems configuration. This position is fully remote within the U.S. The salary range for this role is $90,000 –$120,000. Candidates will be paid within this range based on their work experience and skills. Candidates are also eligible for our full list of benefits linked here.

Responsibilities

• Design, build, and scale sophisticated high-volume server-side and front-end applications and frameworks, including those integrating AI/ML models for computer vision tasks.
• Collaborate with cross-functional teams to identify opportunities where computer vision can enhance product functionality or user experience.
• Develop APIs or interfaces that allow seamless interaction between front-end applications and deployed computer vision models.
• Stay updated on advancements in computer vision technologies and apply state-of-the-art techniques to improve application performance.
• Develop and deploy AI-driven features such as image recognition, object detection, and video analysis by embedding machine learning models into web applications.
• Collaborate with machine learning engineers to preprocess data, train models, and deploy them into production environments.
• Write reusable, testable, and maintainable code for both traditional application features and AI/ML pipelines.
• Ensure code meets test coverage standards while validating the performance of AI/ML models using metrics like accuracy, precision, and recall.
• Conduct analysis to determine integration needs for AI/ML solutions and ensure seamless interaction between back-end services and machine learning APIs.
• Work with project/delivery leads to optimize deployment pipelines for AI/ML models, ensuring scalability and low-latency performance in production systems.
• Monitor and refine deployed models to improve accuracy and efficiency over time using telemetry data and dashboards.
• Incorporate security best practices into the development of AI/ML-enabled applications to safeguard sensitive data.
• Participate in Agile ceremonies such as sprint planning, daily stand-ups, grooming sessions, and retrospectives while addressing AI/ML-specific challenges.

Requirements

• 5+ years’ experience developing full-stack applications using NodeJS, Angular, JavaScript, or similar technologies.
• Familiarity with Python for building or integrating machine learning models is a strong plus.
• Experience consuming and creating APIs (REST, GraphQL) to integrate machine learning solutions into web applications.
• Knowledge of NoSQL and SQL databases; experience managing large datasets used for training ML models is a plus.
• Understanding of container-based application deployments for deploying ML systems in scalable environments.
• Experience in microservices development with a focus on integrating AI/ML components into modular architectures.
• Proficiency in version control tools like GitLab/GitHub and CI/CD pipelines (e.g., GitLab CI/CD, Jenkins) with experience automating model deployment workflows.
• Strong problem-solving skills to debug issues in both traditional software systems and AI/ML pipelines.

Nice-to-haves

• Hands-on experience with machine learning frameworks such as TensorFlow or PyTorch for developing or fine-tuning computer vision models (e.g., CNNs).
• Familiarity with computer vision libraries like OpenCV for preprocessing images or videos before feeding them into ML models.
• Knowledge of cloud platforms like AWS or Azure for deploying scalable AI/ML solutions using services.
• Experience in zero-downtime deployments of AI/ML features with techniques like feature flagging or A/B testing.
• Understanding of deep learning architectures used in computer vision tasks such as object detection (e.g., YOLO), image classification, or segmentation.