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Common mid-level AI title, metro hiring, and moderate brand drive high competition.
Specialized LLM, RAG, and cloud AI engineering skills strongly favor ML-specific backgrounds.
Specific LLM/RAG/cloud tooling and 5+ years experience create stringent mandatory filters.
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Design, build, deploy, and support production-grade GenAI and agentic AI solutions integrating LLMs, retrieval-based patterns, APIs, and workflows.
Own development of scalable, reliable AI-powered product capabilities using Azure OpenAI, LangChain, Semantic Kernel, and similar tools.
Collaborate with Lead AI Engineers and architects to implement, operationalize, and improve AI systems in enterprise environments.
5 to 8+ years of software engineering or AI/ML engineering experience with hands-on AI solution delivery.
Practical experience with GenAI, LLM-powered, or AI-enabled solution development and deployment.
Strong Python backend skills, API development experience, and familiarity with orchestration frameworks like LangChain, Semantic Kernel, or AutoGen.
Experience with cloud-native AI services deployment on platforms such as Azure Functions and knowledge of CI/CD, containerization, and observability tools.
Deep expertise developing and operationalizing complex LLM-powered applications, including RAG and multi-step agentic workflows.
Experience working in enterprise-scale environments with integration of AI into business workflows and operational support responsibilities.
Ability to translate AI solution designs into maintainable, scalable technical implementations and communicate trade-offs to technical and non-technical stakeholders.