Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Mid-level AI role, metro location, known employer and popular title increase applicant competition despite niche LLM specialization.
Specialized production LLM and cloud deployment skills are transferable but favor ML/AI backgrounds.
Explicit 5+ years, required production LLM portfolio, Azure/Databricks, vector DBs, and deployment skills make filters stringent.
Build and deploy production-grade generative AI and agentic AI applications automating enterprise workflows from design through deployment.
Design and implement Retrieval-Augmented Generation (RAG) pipelines including chunking, hybrid search, re-ranking, memory, and tool orchestration.
Deploy, monitor, and maintain AI workloads on Azure Kubernetes Services with CI/CD, observability, and implement responsible AI guardrails such as prompt-injection defense and content filtering.
5+ years of ML/AI engineering experience with production-level large language model (LLM) and agentic AI delivery.
Advanced Python programming skills and experience with at least one agent framework (e.g., LangGraph, AutoGen, CrewAI, PydanticAI).
Expertise in LLM and prompt engineering (GPT, Claude, LLaMA) including hands-on experience with RAG workflows.
Experience with Azure AI stack (Azure OpenAI, AI Search, AI Services), containerized deployment (Docker/Kubernetes on AKS/ARO), Databricks ML tools, version control, and CI/CD pipelines.
Hands-on builder focused on delivering measurable business impact through production AI deployments (portfolio of 3+ deployments).
Strong operational focus on end-to-end AI system design including responsible AI practices and performance/cost evaluation metrics.
Experience working within agile environments deploying scalable AI solutions on Azure cloud with strong engineering discipline in CI/CD and container orchestration.