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Mid-level, metro location, and specialized but market-visible skills create moderate applicant competition.
Production LLM agent expertise is specialized, limiting easy cross-industry transfer despite transferable infra skills.
Mandatory production LLM agent experience plus Databricks, AWS, and IaC requirements make filters very strict.
Design, build, and productionize Large Language Model (LLM) agents including tool-use loops, retrieval, orchestration, evaluation, and guardrails.
Develop production-grade services and APIs in Python and Node/TypeScript, deploy and operate on AWS using Infrastructure as Code (IaC) and CI/CD pipelines.
Own code from commit to production, contribute to a shared codebase with code reviews, and maintain engineering standards in a data and automation infrastructure platform running on Databricks and AWS.
5-6 years of software engineering experience with proven coding skills in Python and Node/TypeScript writing clean, tested, production-grade code.
Demonstrated experience building and shipping production-grade LLM agents with concrete knowledge of agent loops, retrieval, and evaluation in production environments.
Experience deploying and operating on AWS using Infrastructure as Code (Terraform, CDK or equivalent), familiarity with AWS basics (S3, IAM, networking), and CI/CD practices.
Working proficiency with Databricks platform: building and running notebooks and jobs, understanding Unity Catalog namespace, and performing Delta Lake operations.
Experienced senior software engineer who can independently scope, execute, and ship complex code changes in LLM agent and data automation domains.
Has hands-on expertise with real deployed LLM agent systems, able to explain trade-offs and failure modes in production environments.
Comfortable working with cloud-native data infrastructure (AWS, Databricks) and following engineering best practices including code reviews and documentation.