Senior AI Engineer - Azure; Enterprise AI & Secure Systems | W2 ; No OPT's

Position: Senior AI Engineer - Azure (Enterprise AI & Secure Systems) | W2 Only (No OPT's please) |
We are seeking a highly skilled AI Engineer with deep expertise in Microsoft Azure AI ecosystem, who has hands-on experience collaborating with Infrastructure and Information Security (Info Sec) teams. The ideal candidate will drive the design, development, and deployment of secure, scalable AI-powered applications and intelligent widgets across enterprise environments.

Key Responsibilities
Design, develop, and deploy AI/ML solutions using Azure services such as Azure OpenAI, Azure ML, Cognitive Services, and AI Studio.
Build and integrate AI-powered applications, copilots, and widgets into enterprise platforms.
Collaborate closely with Infrastructure teams to ensure scalability, performance, and cloud optimization.
Partner with Info Sec teams to ensure AI solutions comply with security, governance, and regulatory standards.
Implement secure AI architectures, including data protection, identity management, and access controls.
Develop APIs and microservices for AI model integration into business applications.
Optimize model performance, cost, and latency in production environments.
Establish MLOps pipelines for continuous integration, deployment, monitoring, and retraining of models.
Ensure responsible AI practices including data privacy, bias mitigation, and auditability.
Provide technical leadership and mentor junior engineers.
Required

Skills & Qualifications
Strong experience with Microsoft Azure AI stack (Azure OpenAI, Azure ML, Cognitive Services).
Proficiency in Python and frameworks like FastAPI, Flask, or similar.
Experience in building LLM-based applications, prompt engineering, and RAG architectures.
Hands-on experience working with Infrastructure teams (cloud architecture, networking, containers, Kubernetes).
Strong exposure to Info Sec practices (IAM, encryption, secure coding, compliance frameworks).

Experience with API development, microservices, and distributed systems.
Familiarity with Dev Ops/MLOps tools (CI/CD pipelines, Docker, Kubernetes, Git Hub Actions, Azure Dev Ops).
Solid understanding of data engineering concepts and databases (SQL/No

SQL).

Preferred Qualifications

Experience with enterprise AI adoption and governance frameworks.
Knowledge of Zero Trust Architecture and cloud security best practices.
Exposure to multi-cloud or hybrid cloud environments.
Experience building chatbots, copilots, or AI-driven UI widgets.

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