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Lead AI Engineer

$70–$80 an hour

Eliassen Group · Charlotte, NC · Hybrid · Contract

Build · Found · posted 8 days ago

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Experience asked for: at least 10 years

Read out of the requirements below, in the employer's own words — not from a dropdown. Where a posting lists several requirements we take the largest, because a requirements list is a list of things you need all of.

Description Hybrid 4/1 in Charlotte, NC Our client seeks a Lead AI Engineer to guide IVR and conversational AI initiatives and to design scalable LLM and NLP solutions for high-volume customer interactions. The role will drive architecture, best practices, and innovation across voice AI systems while contributing as an individual contributor and mentoring a distributed team. This is a contract to hire opportunity. Applicants must be willing and able to work on a w2 basis and convert to FTE following contract duration. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance. Rate: $70.00 to $80.00/hr. w2 Responsibilities • Architect and develop AI-driven IVR and conversational systems using Python. • Build NLP models for text classification and clustering to support intent detection and call routing. • Perform data analysis using SQL on large-scale IVR and interaction datasets. • Design and deploy LLM workflows, including prompt engineering, RAG, and evaluation for voice and chat use cases. • Lead development of agentic AI workflows for multi-step customer interactions. • Establish LLMOps best practices for monitoring, evaluation, and lifecycle management. • Partner with IVR platform, speech engineering, and product teams. • Mentor offshore and junior AI engineers. Experience Requirements • 10+ years of experience in AI and ML with pre-LLM era exposure. • Strong proficiency in Python. • Proven expertise in NLP, including text classification and clustering. • Strong experience in data analysis using SQL. • Hands-on experience with LLMs, including prompt engineering, evaluation frameworks, and Retrieval-Augmented Generation. • Good to have: experience with agentic AI and multi-agent orchestration. • Good to have: experience with LLMOps or MLOps in production environments. • Good to have: familiarity with IVR platforms and speech technologies such as ASR, NLU, and TTS. • Tools and libraries: NumPy, Pandas, Scikit-learn, XGBoost, spaCy, Hugging Face Transformers, NLTK, Sentence-Transformers, LangChain, LlamaIndex, OpenAI SDK, LangGraph, AutoGen, CrewAI, Oracle, MySQL, SQLAlchemy, FAISS, Pinecone, Weaviate, Qdrant, MLflow, Weights & Biases, BentoML, Airflow. Education Requirements

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