AI Automation Engineer / Sr. AI Automation Engineer
New
J
JobgetherSecurity & IT
Based in the United StatesFull-TimeSenior
SalaryCompetitive base salary ranging from $80,000 to $160,000 USD
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLGCPCI/CD
Requirements
- 5+ years of experience designing, building, and deploying production software, automation solutions, or data-driven applications.
- Strong programming skills in Python and solid experience working with SQL and enterprise data environments.
- Hands-on experience deploying and operating cloud-native applications, preferably on Google Cloud Platform (GCP).
- Strong understanding of production engineering fundamentals, including authentication, identity management, secrets management, CI/CD, logging, monitoring, and infrastructure operations.
- Experience integrating enterprise applications through APIs and SaaS platforms.
- Ability to work directly with business stakeholders and translate functional requirements into scalable technical solutions.
- Experience building LLM-powered applications, AI agents, retrieval-augmented generation (RAG) pipelines, tool/function calling, or agentic workflows.
- Familiarity with AI evaluation and observability platforms such as LangSmith, Langfuse, Braintrust, or similar tools.
- Experience with vector databases and retrieval technologies, including Vertex AI Vector Search, Pinecone, pgvector, or equivalent solutions.
Responsibilities
- Partner with business teams to transform AI proofs of concept into secure, scalable, production-ready applications, including dashboards, reporting tools, chatbots, support agents, and internal business applications.
- Design, develop, and deploy reusable AI agents, automation workflows, and intelligent integrations powered by large language models (LLMs).
- Integrate AI capabilities with enterprise platforms and business systems, including CRM, collaboration, ERP, and database solutions.
- Build and maintain cloud-native applications on a governed Google Cloud Platform (GCP) environment, ensuring reliability, scalability, and operational excellence.
- Implement authentication, access controls, secrets management, CI/CD pipelines, monitoring, logging, and version control to harden applications for production.
- Develop automated processes to maintain software dependencies, improve application security, and reduce operational overhead.
- Create evaluation frameworks, regression testing, and observability solutions to monitor AI model performance, retrieval quality, application health, and overall system reliability.
- Collaborate with cross-functional stakeholders to translate evolving business requirements into maintainable, high-quality software solutions while continuously improving AI engineering practices.
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