About The Position
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 (InfoSec) teams. The ideal candidate will drive the design, development, and deployment of secure, scalable AI-powered applications and intelligent widgets across enterprise environments.
Requirements
- 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 InfoSec practices (IAM, encryption, secure coding, compliance frameworks).
- Experience with API development, microservices, and distributed systems.
- Familiarity with DevOps/MLOps tools (CI/CD pipelines, Docker, Kubernetes, GitHub Actions, Azure DevOps).
- Solid understanding of data engineering concepts and databases (SQL/NoSQL).
Nice To Haves
- 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.
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 InfoSec 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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
Number of Employees
1-10 employees
