Description

Lead the design and implementation of generative AI solutions on Azure and AWS, ensuring scalability, efficiency, and observability for LLM-based systems.

Responsibilities

• Architect and develop generative AI solutions focused on scalability and efficiency on Azure/AWS cloud platforms.
• Implement and maintain containerized AI applications using Docker, managing deployments via Kubernetes.
• Design observability frameworks for LLMs to monitor performance metrics, system health, and usage patterns.
• Lead performance tuning, troubleshooting, and optimization of generative AI systems.
• Guide and mentor AI engineers in adopting best practices for secure, scalable, and maintainable AI deployments.

Required Skills

• 10+ years of hands-on experience in software development with a strong focus on AI.
• Deep expertise in Azure or AWS cloud architectures, including management tools and services.
• Solid programming skills in Python, with expertise in AI/ML frameworks.
• Experience with Azure OpenAI or AWS Bedrock.
• Proficiency with Docker and Kubernetes for orchestrating containerized applications.
• Demonstrable experience implementing and monitoring Large Language Models (LLMs).
• Experience implementing CI/CD pipelines and familiarity with DevOps practices.

Preferred Skills

• Azure/AWS certifications (e.g., Azure Solutions Architect, AWS Solutions Architect, AI Practitioner).
• Experience with LLM observability tools (e.g., Langfuse, Datadog).
• Knowledge of machine learning model lifecycle management and MLOps principles.

Key Skills

Education

Any Graduate