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Advance agentic capabilities by designing autonomous systems capable of reasoning, planning, and interacting with complex environments. Prototype and refine novel algorithms to enhance model performance, scalability, and interpretability.

Collaborate with cross-functional teams to translate theoretical AI advancements into practical enterprise applications. Conduct rigorous experimentation and benchmarking to evaluate model efficacy and robustness.

Publish research findings, contribute to open-source initiatives, and stay updated on emerging AI trends. Ensure ethical AI development by integrating fairness, transparency, and accountability into model design. Optimize training pipelines and infrastructure for large-scale model deployment and continuous learning. Partner with data engineering and product teams to integrate AI solutions into production systems.

Possess an undergraduate degree or equivalent experience, with a minimum of 5 years in software development and over 2 years focused on AI/ML solutions. Hands-on experience with Azure Functions, Azure Monitor, and App Insights is essential.

Proficiency in cloud-based AI and data services including Python, Databricks, RAG, Agentic, AI Foundry, and LangFuse. Familiarity with GitHub and M365 Copilot is also required.

Experience with deep learning frameworks like PyTorch, TensorFlow, and Scikit-learn. Experience with frontend development (React or Angular) and backend services (Java or .NET) is beneficial. Ability to collaborate with senior AI/ML engineers to develop new models from scratch or enhance existing ones.

Extensive experience implementing generative AI solutions using OpenAI compatible APIs, with a solid understanding of enterprise search, NLP, Speech AI, Vision AI, and various MLS techniques. Expertise in Azure Document Intelligence, OCR, Databricks, and advanced Python for AI/ML and automation.

Demonstrated ability to lead the design and implementation of intelligent applications using Azure OpenAI, LLM Gateway, and Retrieval Augmented Generation (RAG) techniques.