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Niche agentic LLM and Azure/Kubernetes production expertise narrows the qualified candidate pool.
Highly specialized ML/AI agent and Azure production requirements reduce industry transferability.
Multiple hard requirements: 7+ years, 2+ years agentic LLM, Azure, Kubernetes, RAG, and security guardrails.
Design, build, and deploy production-grade agentic AI applications on Azure, integrating LLMs with client systems and ensuring performance, security, and cost-efficiency.
Establish evaluation metrics for AI agents focusing on accuracy, groundedness, and task completion; implement production instrumentation and responsible AI guardrails.
Lead technical direction on client projects, mentor junior engineers, convert ambiguous business needs into scoped agent use cases, and collaborate across teams for end-to-end delivery.
Minimum 7 years in software, data, or ML engineering with at least 2 years building LLM-based or agentic applications used by real users.
Strong hands-on Python development experience including testing, packaging, and code review practices.
Proven experience deploying and operating AI services on Azure including AKS, Docker, Kubernetes, and implementing agent design patterns and RAG systems.
Work Experience Required: Minimum 7 years in related engineering roles as stated above.
Experienced in designing multi-step, retrieval-augmented, and function-calling agent systems on Azure with solid knowledge of agent frameworks and APIs.
Capable of consulting with clients to translate complex, ambiguous problems into practical AI agent solutions with clear technical communication skills.
Skilled in production-grade software engineering practices (CI/CD, version control) and managing multiple client projects in a consulting environment.