Job Description
Job DescriptionJob Overview: Pay Range: $60hr - $65hr Responsibilities:
- Develop, fine‑tune, and evaluate machine learning and LLM‑based models.
- Build end‑to‑end AI pipelines including data preprocessing, feature engineering, training, and monitoring.
- Design and optimize prompting strategies, RAG pipelines, or agent‑based workflows for LLM applications.
- Implement production‑ready AI services and APIs using scalable architectures.
- Collaborate with product managers and engineers to identify AI use cases and translate requirements into technical solutions.
- Monitor model performance and drift, implementing continual improvements.
- Conduct research into emerging AI techniques and tools to recommend adoption where appropriate.
- Ensure responsible AI practices including fairness, safety, privacy, and compliance.
Required Qualifications:
- Bachelor’s or Master’s in Computer Science, Machine Learning, Engineering, or a related field.
- Experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX.
- Strong software engineering skills in Python, plus experience with APIs, cloud services (Azure, AWS, or GCP), and containerization.
- Hands‑on experience building applications using LLMs (e.g., OpenAI, Azure OpenAI, Hugging Face, Anthropic).
- Familiarity with vector databases (e.g., FAISS, Milvus, Pinecone) and retrieval‑augmented generation (RAG).
- Experience deploying AI models to production environments.
- Strong understanding of data structures, algorithms, and system design.
Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading staffing and recruiting powerhouse. Proudly recognized as a nationally and locally certified diversity firm, Cynet delivers agile, scalable talent solutions across industries. With an active footprint in all 50 U.S. states and Canada, we support thousands of consultants through our expansive, high-performing recruitment engine operating across North America and Asia—ensuring speed, quality, and consistency in every hire.