JR Software Solutions
Job Title:
Journeyman AI/ML Engineer - Full Stack Location:
Within ~2 Hours of Atlanta, GA Employment Type:
Full-Time / Contract Clearance:
Not required
About the Role We are seeking a
Journeyman-level AI/ML Engineer
with strong
Python
programming,
React-based front-end development , and full-stack experience to join our growing team. This role will support the design, development, and deployment of intelligent, scalable applications across diverse environments. Candidates should be comfortable contributing to both the machine learning pipeline and the user-facing experience. Key Responsibilities Design and implement machine learning models and inference pipelines using Python (TensorFlow, PyTorch, or similar). Collaborate with frontend teams to build user interfaces using
React . Contribute to full-stack application development (REST APIs, databases, deployment pipelines). Integrate AI/ML components into production systems with proper data engineering support. Optimize system performance and model accuracy with iterative experimentation. Participate in Agile/Scrum development cycles and cross-functional team discussions. Support code reviews, unit testing, and documentation efforts. Required Qualifications 5+ years of professional experience in software development or ML engineering. Proficient in
Python , with a strong understanding of ML libraries (scikit-learn, pandas, NumPy). Experience building web apps with
React.js
and JavaScript/TypeScript. Knowledge of back-end frameworks like
FastAPI ,
Django , or
Flask . Familiarity with cloud platforms (AWS, GCP, or Azure) and CI/CD pipelines. Solid understanding of REST APIs, Git workflows, and relational/non-relational databases. Bachelor's degree in Computer Science, Engineering, Mathematics, or related field (or equivalent experience). Nice to Have Experience deploying ML models in production environments. Familiarity with Docker, Kubernetes, and modern DevOps practices. Prior experience with data visualization tools or libraries (D3.js, Plotly). Exposure to MLOps tools (MLflow, Kubeflow, SageMaker).
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Journeyman AI/ML Engineer - Full Stack Location:
Within ~2 Hours of Atlanta, GA Employment Type:
Full-Time / Contract Clearance:
Not required
About the Role We are seeking a
Journeyman-level AI/ML Engineer
with strong
Python
programming,
React-based front-end development , and full-stack experience to join our growing team. This role will support the design, development, and deployment of intelligent, scalable applications across diverse environments. Candidates should be comfortable contributing to both the machine learning pipeline and the user-facing experience. Key Responsibilities Design and implement machine learning models and inference pipelines using Python (TensorFlow, PyTorch, or similar). Collaborate with frontend teams to build user interfaces using
React . Contribute to full-stack application development (REST APIs, databases, deployment pipelines). Integrate AI/ML components into production systems with proper data engineering support. Optimize system performance and model accuracy with iterative experimentation. Participate in Agile/Scrum development cycles and cross-functional team discussions. Support code reviews, unit testing, and documentation efforts. Required Qualifications 5+ years of professional experience in software development or ML engineering. Proficient in
Python , with a strong understanding of ML libraries (scikit-learn, pandas, NumPy). Experience building web apps with
React.js
and JavaScript/TypeScript. Knowledge of back-end frameworks like
FastAPI ,
Django , or
Flask . Familiarity with cloud platforms (AWS, GCP, or Azure) and CI/CD pipelines. Solid understanding of REST APIs, Git workflows, and relational/non-relational databases. Bachelor's degree in Computer Science, Engineering, Mathematics, or related field (or equivalent experience). Nice to Have Experience deploying ML models in production environments. Familiarity with Docker, Kubernetes, and modern DevOps practices. Prior experience with data visualization tools or libraries (D3.js, Plotly). Exposure to MLOps tools (MLflow, Kubeflow, SageMaker).
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