Overview
On Site
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 18 Month(s)
Skills
AI/ML
LLMs
RAG techniques
and agentic AI frameworks
chunking strategies
GenAI
ython
PyTorch
TensorFlow
Job Details
Title : AI/Ml Engineer
Location : Nashville TN
Duration : Long Term
Job Summary:
We are looking for a skilled AI/ML Engineer with 3 4 years of hands-on experience in building and deploying intelligent systems. The ideal candidate will be well-versed in AI/ML concepts, Retrieval-Augmented Generation (RAG), chunking strategies, and large language models (LLMs). You will contribute to the design and development of scalable AI applications and agentic frameworks with real-time data processing capabilities.
Key Responsibilities:
- Design, develop, and implement ML models and GenAI solutions.
- Develop and optimize chunking strategies and validation pipelines for RAG-based systems.
- Integrate LLM applications with retrieval techniques such as RAG, Amazon Kendra, and vector databases.
- Conduct real-time image processing for discrepancy detection and classification.
- Build and maintain robust pipelines for data preprocessing and model validation.
- Collaborate with cross-functional teams to translate business requirements into technical solutions.
- Stay up to date with emerging AI/ML trends, tools, and best practices.
Required Skills & Expertise:
- Solid understanding of AI/ML concepts including neural networks, supervised and unsupervised learning.
- Strong experience with LLMs, RAG techniques, and agentic AI frameworks.
- Hands-on experience with chunking strategies for text or data segmentation.
- Proficiency in real-time image processing and anomaly/discrepancy detection.
- Experience with GenAI solutions and prompt engineering.
- Familiarity with vector databases (e.g., FAISS, Pinecone)
- Expertise in data analysis and preprocessing techniques.
- Excellent communication skills and ability to clearly convey complex AI/ML concepts.
Preferred Qualifications
- Experience with cloud platforms (AWS, Google Cloud Platform, Azure) for deploying ML workloads.
- Knowledge of Python, PyTorch, TensorFlow, or similar ML frameworks.
- Exposure to open-source LLM frameworks (LangChain, Haystack, LlamaIndex, etc.)
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