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Tier-1 employer and mid-level senior title attract applicants, but niche RAG/agent skills temper competition.
Highly domain-specific ML/AI and LLM experience required, limiting cross-industry transferability.
Explicit 5-year requirement plus mandatory LLM, agent frameworks, Kubernetes, and CI/CD skills makes filters strict.
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Develop and optimize GPU-accelerated, scalable Retrieval Augmented Generation (RAG) workflows focusing on accuracy, relevance, and performance.
Design and implement AI agent systems that enable reasoning, planning, and multi-step execution within the RAG pipeline.
Build and deploy end-to-end disaggregated RAG pipelines on microservices architecture including Kubernetes, and drive continuous pipeline improvements through evaluation and strategic enhancements.
5+ years of professional software engineering experience with strong expertise in Python and AI applications.
Bachelor's or Master's degree in Computer Science, Electrical Engineering, Data Science, Artificial Intelligence, or related fields.
Hands-on experience with building and deploying LLM-powered AI applications, RAG, or Agentic AI workflows and knowledge of LLM design patterns.
Experience with microservices, Docker, Helm, Kubernetes, and end-to-end software lifecycle including CI/CD pipelines.
Proven ability to design multi-agent systems and complex workflow orchestration engines in AI contexts.
Familiarity with AI observability tooling, evaluation frameworks, and MLOps pipelines enhancing production-grade AI systems.
Experience deploying AI models across data center, cloud, and embedded system environments and strong Python coding skills with AI coding agents.