What are the responsibilities and job description for the Mid-Level AI / Machine Learning Software Engineer position at Modern Technology Solutions, Inc. (MTSI)?
We are seeking a Mid-Level AI / Machine Learning Software Engineer to support development of scalable data analysis and machine learning capabilities across large datasets and real-time data streams. The role focuses on designing, implementing, and optimizing machine learning models and data pipelines using Python and modern deep learning frameworks.
The ideal candidate has strong programming fundamentals, hands-on model development experience, and is comfortable working with large structured and unstructured datasets in production environments.
Primary Responsibilities
The ideal candidate has strong programming fundamentals, hands-on model development experience, and is comfortable working with large structured and unstructured datasets in production environments.
Primary Responsibilities
- Design, develop, and maintain Python-based data processing and analytics solutions
- Implement and optimize machine learning and deep learning models
- Work with large datasets and streaming data sources
- Develop reusable data structures and efficient algorithms for analysis workflows
- Build and evaluate models for classification, prediction, and pattern recognition
- Integrate AI/ML capabilities into software systems and pipelines
- Collaborate with software engineers, data engineers, and analysts to deploy solutions
- Perform model validation, performance tuning, and debugging
- Document architecture, implementation, and usage of developed tools
- 3 years of professional software development experience
- Strong Python development skills
- Experience working with large datasets and/or streaming data
- Proficiency in machine learning and deep learning frameworks:
- PyTorch
- TensorFlow
- Keras
- Hugging Face Transformers
- Understanding of machine learning concepts and model architectures, including:
- Decision Trees / Random Forests
- LSTM / sequence models
- Experience implementing, training, and evaluating ML models
- Knowledge of data structures, algorithms, and performance optimization
- Familiarity with version control (Git) and collaborative development workflows
- Experience with Retrieval-Augmented Generation (RAG)
- Experience with Model Context Protocols (MCP) or similar agent/tool interaction frameworks
- Experience with GPU acceleration and CUDA architecture
- Drivers, runtime, and APIs
- Experience with deep learning and reinforcement learning libraries
- Experience building or consuming real-time data pipelines
- Data visualization and exploratory analysis (Matplotlib, Seaborn, Plotly, etc.)
- Familiarity with model deployment and inference optimization
- Experience working in containerized or distributed environments
- Bachelor’s degree (or working toward a degree) in Computer Science, Data Science, Engineering, Mathematics, or related field
- (Equivalent practical experience considered)
- Linux development environments
- Jupyter notebooks
- Docker or container basics
- Basic command line usage