AI/NLP Engineer Remote (Ohio, USA) Contract

Job Title: AI/NLP Engineer / Developer Location: Remote (Ohio, USA) Employment Type: Contract, Long Term Prior experience with Cardinal is highly preferred.

Job Overview: We are seeking an experienced AI/NLP Engineer to design, develop, and integrate advanced AI solutions leveraging Azure Cognitive Services, Large Language Models (LLMs), and NLP frameworks.

The ideal candidate will have hands-on experience with Databricks, SQL Server, and modern AI/ML pipelines, capable of building intelligent, scalable, and production-ready solutions.

Key Responsibilities: Design and implement AI and NLP-based solutions integrated with Azure Cognitive Services and Databricks. Develop, fine-tune, and deploy LLMs and NLP models using frameworks like PyTorch, LangChain, and OpenAI APIs.

Work with offline open-weight models and integrate them within enterprise data workflows. Implement Claude API, OpenAI API, and other third-party model integrations for conversational and generative AI use cases.

Architect and automate data engineering pipelines to support ML/AI workloads. Integrate Databricks Foundational Models into enterprise data ecosystems for model training, inference, and monitoring.

Collaborate with data scientists, engineers, and architects to ensure model scalability, performance, and compliance. Monitor and optimize model performance and implement retraining strategies as needed.

Must-Have Skills: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Overall IT Experience: 8-12 Years Expertise in Python and NLP model development (Transformers, LLMs, etc.).

Strong experience with Azure Cognitive Services integration. Hands-on experience with Databricks and SQL Server. Experience with Offline Open Weight Models and Databricks Foundational Model Integration. Proficiency in Claude API, OpenAI API, LangChain, and PyTorch.

Knowledge of AI/ML architecture and data pipeline design for scalable solutions. Nice-to-Have Skills: Experience with Vector Databases (FAISS, Pinecone, etc.). Familiarity with MLOps, Model Monitoring, and LLM fine-tuning.

Exposure to Azure Machine Learning, Kubernetes, and Docker for deployment. Understanding of Responsible AI principles, model governance, and security.