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Mid-level, metro-based DevOps AI platform role with broad cloud and MLOps requirements increases candidate competition.
Core cloud and DevOps skills transfer across industries, but LLM/agent expertise raises domain specificity.
Multiple mandatory technologies (AWS, IaC, Kubernetes, MLOps) and explicit 5+ years make shortlisting strict.
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Build, deploy, and operate AWS-based AI/Agentic platform services enabling multi-agent AI workflows, focusing on backend, orchestration, and infrastructure as code.
Own reliability and production readiness of platform services including monitoring, logging, alerting, and incident response for AI model and agent performance.
Design reusable service abstractions and SDKs, support shared platform features, and collaborate with application teams to integrate new AI capabilities and improve platform usability.
Bachelor’s or Master’s degree in CS, AI/ML, Data Science, or equivalent experience.
5+ years of industry experience in software engineering or machine learning.
Strong AWS (or GCP/Azure) skills including compute, eventing, API management, IAM, containerization (Docker/Kubernetes), and Infrastructure-as-Code (CDK or Terraform).
Proficiency in backend development using languages like Python, Java, Go, Node.js, or TypeScript, with experience in microservices, event-driven architectures, and API design.
Experienced in agent frameworks (e.g., LangChain, LangGraph, CrewAI) with practical platform operation skills including RAG, memory systems, and human-in-the-loop workflows.
Demonstrated ability to build and maintain CI/CD and MLOps pipelines for deploying and scaling LLM-powered services.
Skilled in platform governance and security controls around cost, permissions, and preventing prompt-injection or agent attacks, with aptitude for mentoring engineers and shaping engineering standards.