Artificial Intelligence Consultant
Staffing Technologies, Atlanta, GA, United States
The AI/ML Developer will leverage Mathematical Programming and AI/DL Algorithms and Technologies to drive research and development, addressing complex business challenges. This role focuses on utilizing cutting-edge methodologies and technologies to create solutions.
Focuses on designing, developing, and implementing machine learning models and AI applications to solve business problems. Here are some key responsibilities:
- Designing and Developing Models
- Creating machine learning and deep learning models to address specific business needs.
- Data Analysis
- Analyzing large datasets to extract meaningful insights and improve model performance.
- Algorithm Implementation
- Implementing AI/ML algorithms to solve complex problems.
- Collaboration
- Working closely with cross-functional teams to integrate AI solutions into existing systems.
- Model Testing and Validation
- Running experiments to test the effectiveness of models and making necessary adjustments.
- Automation: Identifying repetitive tasks and developing automated solutions to increase efficiency.
Qualifications:
- Educational Background:
Preferred Master's or Ph.D. in Science and Engineering disciplines such as Mathematics, Physics, Economics, Computer Science, or related fields with a focus on AI/ML.
- Business Problem Solving:
Expertise in defining business problems with mathematics including, but not limited to, risk and cost management, price and revenue optimization, sales/operational process mining and simulation, and time series forecasting.
- AI/ML Technology Proficiency:
Hands-on experience with advanced AI/ML tools and frameworks like Sciki-Learn, TensorFlow, Keras, PyTorch, RAG, Langchain, LlamaIndex, Neo4j, and expertise in NLP, LM, CV techniques.
- Programming Skills:
Proficient in programming languages and platforms like Python, PySpark, Scala, C++, CUDA, SQL/NoSQL/Vector DB.
- Algorithm Expertise:
Strong knowledge of AI/ML algorithms including Bayesian Inference, XGBoost, CNNs, RNNs, GNNs, KG, Generative Modeling, RAG.
- DevOps and Cloud Tools:
Familliar with platforms and tools like Docker, Kubernetes, GitHub Actions, Terraform, GPU (CUDA/Nemo/NIM), Azure (Azure ML, Azure AI, Databricks).