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Tier-1 brand, metro location, and sought-after AI platform skills create moderate applicant competition.
Highly specialized ML/LLM infrastructure skills limit cross-industry transferability.
10+ years, principal seniority, and mandatory ML infra plus Kubernetes and systems languages imply high selectivity.
Lead architecture, deployment, and evolution of large-scale Generative AI platforms and intelligent agent systems in production.
Design and implement high-performance, scalable, secure AI systems including MCP servers standardizing interactions between LLMs, enterprise tools, and local resources.
Define engineering standards, architectural patterns, and best practices for enterprise AI platforms, and build critical platform components in collaboration with cross-functional teams.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field, or equivalent practical experience.
10+ years of software engineering experience with expertise in distributed systems, platform engineering, or machine learning infrastructure.
Proven experience building, deploying, and operating production-grade AI/ML or LLM systems at scale.
Strong skills in Python and one or more systems languages (Go, C++, Rust, Java) plus hands-on experience with Docker, Kubernetes, and cloud-native deployment.
Experienced in building and running production AI/ML systems using containerized infrastructure and advanced optimization techniques like model quantization and KV-cache.
Familiar with enterprise AI system safety, observability, and evaluation frameworks targeting large language models and retrieval-augmented generation (RAG) systems.
Capabilities include balancing strategic architecture decisions with hands-on execution, thriving in ambiguous and dynamic environments, and collaborating effectively across multidisciplinary teams.