Cloud Engineer III- AI

Key Roles and Responsibilities

• Architect enterprise-scale AI/ML infrastructure across AWS, Azure, and Google Cloud Platform, ensuring high availability, scalability, performance, and cost efficiency.
• Lead the design and implementation of cloud infrastructure standards, governance frameworks, security controls, and technical best practices for AI/ML platforms.
• Design scalable Kubernetes-based platforms and self-service environments that enable ML, data science, and application teams to build, deploy, and manage AI workloads independently.
• Architect Generative AI, Retrieval-Augmented Generation, vector database, and LLM deployment solutions using frameworks and tools such as LangChain, CrewAI, LlamaIndex, OpenAI-compatible APIs, and enterprise AI services.
• Very good understanding of Microsoft Foundry
• Build and manage production-grade MLOps and LLMOps pipelines for model training, deployment, monitoring, versioning, evaluation, and continuous improvement.
• Design cloud-native solutions for AI workloads using containerization, orchestration, serverless computing, GPU/accelerator-based compute, managed ML services, and distributed processing frameworks.

Required Skills and Technical Expertise

• Bachelor's degree in Computer Science or equivalent.
• 5-8 years of relevant experience.
• Strong hands-on experience with cloud platforms such as AWS, Microsoft Azure, and Google Cloud Platform and designing large-scale cloud architectures.
• Hands-on experience with GenAI frameworks and orchestration tools such as LangGraph, LangChain, CrewAI, Llama Index, Semantic Kernel, or similar frameworks.
• Experience designing RAG pipelines, embedding workflows, document ingestion pipelines, prompt orchestration, vector search, and enterprise knowledge retrieval solutions.
• Strong understanding of LLM deployment patterns, model serving, inference optimization, prompt engineering, evaluation frameworks, guardrails, and responsible AI controls.
• Strong scripting and programming skills in Python , Bash, PowerShell, or similar languages, with the ability to build automation and integration solutions.
• Ability to work with APIs, data models, and external toolchains across complex systems.

What you can expect