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.

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