AI/ML Engineer, Software Development - Noke

Janus International Group•Atlanta, GA

About The Position

The AI/ML Engineer is responsible for designing, developing, and deploying scalable artificial intelligence and machine‑learning solutions that enhance customer interactions, automate decision-making, and improve operational efficiency. This role involves building intelligent workflows, predictive models, data‑driven insights, and automation frameworks in collaboration with cross‑functional engineering, product, and support teams.

Requirements

3+ years of experience in AI/ML engineering, data science, or intelligent automation.
3+ years of hands‑on experience with modern cloud platforms including compute, data, and AI services.
3+ years of proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or scikit‑learn.
Experience building real‑time, streaming, or event‑driven data pipelines.
Proven track record of designing, deploying, and maintaining production‑grade ML systems.
Knowledge of APIs, microservices, and secure integration patterns.

Nice To Haves

3+ years of Experience with IoT, device telemetry, or distributed sensor data systems.
3+ years of Background in LLM tuning, retrieval‑augmented generation (RAG), and conversational AI.
Familiarity with contact center platforms or enterprise workflow automation systems.
Understanding of authentication, authorization, and access‑control frameworks.

Responsibilities

Design and implement AI‑powered conversational flows and automated decisioning logic.
Build secure data integrations to enable real‑time processing and intelligent responses.
Optimize large language model (LLM) workflows for accuracy, performance, and usability.
Apply machine learning techniques to structured and unstructured data from various systems.
Develop predictive models to identify trends, detect anomalies, and support proactive decision‑making.
Implement monitoring and alerting mechanisms to ensure timely detection and escalation of issues.
Integrate AI‑driven insights into internal tools to support agents and operations teams.
Deliver real-time recommendations, next-best-actions, and contextual guidance based on combined system and interaction data.
Enhance employee productivity through intelligent assistance and dynamic knowledge retrieval.
Build diagnostic and remediation automation with appropriate safety, governance, and auditability controls.
Ensure all automated actions adhere to defined compliance, authorization, and security rules.
Develop dashboards and reporting tools for system health, workflow performance, and model metrics.