AI/ML Engineer

JR Software Solutions, Inc.•Atlanta, GA

About The Position

JRSS is seeking an AI/ML Engineer to join a collaborative team of technologists, data scientists, and stakeholders to tackle meaningful challenges using ML, Generative AI, and modern tools. The role involves contributing to building and scaling intelligent systems, from core ML models to chatbot and Retrieval-Augmented Generation (RAG) applications. The ideal candidate will have strong skills in Python, SQL, and cloud platforms like Azure, and will help deliver practical, forward-looking solutions in dynamic environments.

Requirements

3+ years of experience designing, developing, and deploying machine learning models
3+ years of experience with Generative AI, LLMs, or RAG applications
4+ years of hands-on experience with Python for ML and data engineering
Experience with SQL for data manipulation and feature engineering
Experience with big data tools such as Apache Spark
Experience with Databricks and MLOps tools like MLflow
Experience with cloud, preferably in Azure
Ability to exhibit strong communication and customer-facing skills
Ability to thrive both independently and in cross-functional teams
Ability to problem solve and stay current with emerging ML trends

Nice To Haves

Experience with full-stack development or deploying end-to-end ML applications
Experience with chatbot development or conversational AI
Experience fine-tuning large models
Experience with deploying ML solutions using MLOps pipelines
Knowledge of Agile workflows and tools like JIRA

Responsibilities

Design, develop, and deploy scalable machine learning models and AI-driven solutions to address complex business and operational challenges.
Build and enhance Generative AI, LLM, and Retrieval-Augmented Generation (RAG) applications, including chatbot and conversational AI capabilities.
Develop and optimize data pipelines, feature engineering workflows, and large-scale data processing solutions using Python, SQL, and Spark.
Implement and support MLOps practices, including model training, deployment, monitoring, and lifecycle management using tools such as MLflow and Azure cloud services.
Collaborate with cross-functional teams and stakeholders to deliver customer-focused AI/ML solutions while staying current on emerging technologies and industry best practices.