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# AI Engineer (Hybrid - NYC)

- **Company:** Oscar Technology
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Microsoft Azure, Big Data, Boost (C++ Libraries), Cloud Computing, Cloud Database, Machine Learning, Windows PowerShell, Azure Machine Learning, Search Technologies, Data Processing, Retrieval-Augmented Generation, Prompt Engineering, Generative AI, Machine Learning Operations, Serverless Computing, Databricks
- **Published:** September 10, 2026
- **Apply:** https://www.dice.com/job-detail/bcf7ddd4-408e-43d8-9c6a-2f600771e53d

## About the Role

5-7 years of hands-on experience building AI/ML solutions within enterprise environments.
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Strong Proficiency: Deep technical expertise with C# and .NET.
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Generative AI & RAG: Demonstrated experience in prompt engineering, embeddings, and retrieval-augmented generation architectures.
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Cloud & Tools: Experience using Azure OpenAI, Azure Machine Learning, or equivalent platforms.
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Data Processing: Proven track record working with both structured and unstructured, text-heavy datasets.

Preferred Qualifications
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Hands-on experience with vector databases, semantic search platforms, and frameworks like LangChain.
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Experience with Databricks, large-scale data processing, and cloud automation (Azure Functions, PowerShell).
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Active Microsoft or AI-related certifications.

If you think you'd be a good fit we'd love to hear from you!

## Description

A leading enterprise organization is seeking a Mid-to-Senior AI Engineer to drive generative AI and machine learning initiatives across core corporate verticals, including Finance, Compliance, HR, and Operations. You will work directly with business stakeholders to design, deploy, and optimize high-impact AI solutions, automate workflows, and boost user productivity across the enterprise., *
Design, build, and deploy generative AI and machine learning models tailored to real-world corporate use cases.
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Create and optimize prompts, embeddings, vector search, and retrieval-augmented generation (RAG) strategies.
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Construct and maintain robust pipelines for model training, evaluation, and inference using standard MLOps best practices.
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Collaborate with business leaders to translate complex, high-value requirements into functional, scalable AI applications.
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Ensure responsible AI practices, focusing on data privacy, model governance, and transparency.