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Remote role and mid-level seniority increase applicant pool, but specialized AI-platform and GPU ops skills limit competition.
Role demands specialized AI platform, GPU, and cloud infra experience, limiting cross-industry transferability.
Multiple mandatory technical requirements and 5+ years production AI/cloud experience create high shortlisting strictness.
Design, build, ship, and maintain production AI and backend services on cloud infrastructure with focus on reliability, security, and cost optimization.
Own full lifecycle of AI systems including RAG, fine-tuning, serving, inference optimization, plus cloud architecture and incident response.
Manage cloud and AI spend as engineering constraints by optimizing usage metrics like GPU-hours and cost per token, driving reductions in waste and cost per AI value unit.
5+ years professional software development experience owning production backend services and AI/ML systems (LLMs, RAG, serving).
Proficient in Python plus a second language (TypeScript/Node, Go, or Java); experience with PyTorch or TensorFlow serving frameworks.
Experience with AWS or GCP cloud platforms, Kubernetes, Terraform, and CI/CD pipelines.
Demonstrated experience in controlling or governing cloud and AI expenditures.
Strong engineering discipline with hands-on ownership of production AI infrastructure and application lifecycle management.
Experienced in integrating AI systems into scalable cloud services with observability, security (SOC 2/SOX), and cost governance.
Skilled in balancing operational reliability with cost efficiency in AI platform environments using FinOps practices and infrastructure automation.