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Mid-level, metro location, broad LLM skillset and popular AI role drive high applicant competition.
Requires specialized LLM, ML systems, and deployment experience, making cross-industry transfers difficult.
Explicit 5–9 years, required ML production and LLM experience, and specific tech stack create strict filters.
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Develop and launch agentic AI solutions to diagnose, reason, and take action in large-scale production environments, focusing on reducing risk and operational costs.
Build and productionize LLM-based systems including retrieval, prompt synthesis, validation, self-correction, and integration with runtime tools for observability and incident management.
Implement safety, compliance, and governance mechanisms such as guardrails, policy enforcement, fallbacks, and continuous evaluation to ensure reliable, auditable AI operations.
5+ years software development experience; 3+ years designing, architecting, and launching production ML systems.
Proficiency in Python, C/C++, Go, or Java with strong experience in large-scale Python applications preferred.
Practical hands-on experience with LLMs including API integration, prompt engineering, fine-tuning, RAG and tool-using agent architectures.
Understanding and experience with major LLM frameworks (OpenAI, Gemini, Llama, Qwen, Claude).
Experienced in end-to-end ML system production including deployment, monitoring, and fine-tuning workflows at scale.
Strong background in building complex, safe, and reliable AI agent systems with measurable business impact in operational settings.
Familiarity or experience with cloud-native infrastructure (AWS, container orchestration, serverless, infra-as-code) to support ML production environments.