CAE is seeking to solve complex problems using data-driven approaches and deploy scalable machine learning solutions in production environments, specifically designing scalable NLP systems powered by state-of-the-art transformer models
Requirements
- Familiarity with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn
- Basic understanding of REST APIs and microservice architecture
- Exposure to MLOps tools and practices (e.g., MLflow, Airflow) is a plus
- Experience with Hugging Face Transformers, LangChain, or OpenAI APIs is beneficial but not required
- Comfortable working with cloud platforms (AWS, GCP, or Azure), Linux, and container tools like Docker
- Experience with SQL/NoSQL databases such as MySQL or MongoDB is a plus
- Exposure to deep learning, NLP, or computer vision through coursework or projects
Responsibilities
- Assist in the development and testing of machine learning models for real-world applications under the guidance of senior engineers
- Support the creation and maintenance of data pipelines and model training workflows using modern tools and frameworks
- Participate in model evaluation and validation, helping to analyze performance metrics and suggest improvements
- Help monitor deployed models and contribute to maintaining their reliability and accuracy
- Contribute to the fine-tuning and deployment of large language models (LLMs) for tasks such as summarization, question answering, and chatbots
- Learn and apply tools like ONNX, TensorRT, or vLLM to support efficient model serving
- Assist in implementing retrieval-augmented generation (RAG) pipelines using vector databases (e.g., FAISS, Pinecone)
Other
- Bachelor’s degree in Computer Science, Machine Learning, or a related field
- 0–2 years of experience in software development or machine learning projects (internships or academic projects count)
- Due to U.S. Government contract requirements, only U.S. citizens are eligible for this role
- Incumbent must be eligible for DoD Personal Security Clearance
- Must be able to work overtime on and off-shifts as required
