Senior Applied AI Engineer - Azure AI Apps

Willkie Farr & Gallagher LLP; New York (NY); On-site; USD 190,000 - 210,000; Full time

Job summary

Willkie Farr & Gallagher LLP in New York is seeking a Senior Applied AI Engineer to build, deploy, and support enterprise generative AI applications and workflows. You will collaborate with the Lead Applied AI Architect and cross-functional teams to translate designs into secure, scalable solutions using Python, .NET/C#, and Azure.

The role requires hands-on experience with AI platforms, RAG, agents, and tool integrations, with a focus on latency, reliability, and cost.

Qualifications

• Strong professional development experience with .NET/C# and modern application development practices.
• Strong working knowledge of Python for AI development, integration, data processing, or evaluation.
• Hands-on experience building generative AI applications, including RAG, agents, chatbots, or AI-enabled workflows.
• Experience with platforms such as OpenAI, Azure OpenAI/Microsoft Foundry, Anthropic, or comparable technologies.
• Understanding of LLM concepts including prompting, structured outputs, embeddings, retrieval, grounding, and tool calling.
• Experience with APIs, agentic workflows, and enterprise system integrations; familiarity with MCP is preferred.
• Strong experience with SQL and relational databases and working knowledge of NoSQL platforms such as Azure Cosmos DB.
• Familiarity with vector, semantic, and hybrid search technologies.
• Experience developing and deploying applications in Microsoft Azure, including application hosting, containers, serverless services, identity, secrets, and monitoring.
• Familiarity with generative AI evaluation and testing approaches.
• Understanding of production AI considerations such as latency, token usage, context limits, scalability, reliability, and cost.
• Strong communication and stakeholder engagement skills, with the ability to understand business needs, translate them into practical technical solutions, and independently deliver bespoke AI solutions within established architecture and engineering standards.
• Strong software engineering, troubleshooting, collaboration, and documentation skills.

Responsibilities

• Develop and maintain AI-powered applications, agents, chatbots, and workflow automation using Python and .NET/C#.
• Build RAG and knowledge-grounded solutions, including document ingestion, embeddings, retrieval, search, and response generation.
• Integrate applications with AI platforms such as OpenAI, Azure OpenAI/Microsoft Foundry, Anthropic, or similar technologies.
• Build agentic integrations using tool calling, APIs, enterprise services, and patterns such as Model Context Protocol (MCP).
• Develop reusable APIs, services, connectors, and application components.
• Integrate solutions with SQL databases, Azure Cosmos DB, vector search, enterprise applications, and document repositories.
• Develop and deploy applications using Azure App Service, Container Apps, Functions, Key Vault, Application Insights, API Management, and related services.
• Implement AI-specific testing and evaluation for response quality, retrieval quality, grounding, and tool/agent behavior.
• Monitor and optimize applications for accuracy, latency, reliability, token usage, API/model constraints, and cost.
• Participate in technical design, code reviews, CI/CD, troubleshooting, documentation, and production support.
• Collaborate with technical and business teams and contribute to prototypes and emerging AI initiatives.

Skills

NET/C#

Python

AI development

Azure

OpenAI integration

LLM prompts

APIs integration

Tools

Azure OpenAI

Microsoft Foundry

Anthropic

SQL databases

Cosmos DB

Vector search

Job description

Willkie Farr & Gallagher LLP in New York is seeking a Senior Applied AI Engineer to build, deploy, and support enterprise generative AI applications and workflows. You will collaborate with the Lead Applied AI Architect and cross-functional teams to translate designs into secure, scalable solutions using Python, .NET/C#, and Azure.

The role requires hands-on experience with AI platforms, RAG, agents, and tool integrations, with a focus on latency, reliability, and cost.