Note: The job is a remote job and is open to candidates in USA. Lanthos LLC is hiring a hands-on GCP Generative AI Engineer to build and support applications using Gemini models through Vertex AI for a regulated public-sector program. The role focuses on implementing, testing, monitoring, and documenting AI application features, including RAG workflows, containerized APIs, cloud deployments, integrations, automated evaluations, and operational diagnostics.
Responsibilities
- Build and test Python services that integrate Gemini and other approved models through Vertex AI
- Implement retrieval-augmented generation workflows, including ingestion, chunking, embeddings, retrieval, citations, and grounded responses
- Develop containerized APIs and application components and deploy them to Cloud Run or another approved Google Cloud runtime
- Connect services to approved sources such as Cloud Storage, BigQuery, enterprise search indexes, and internal APIs
- Create automated tests and evaluation checks for retrieval quality, groundedness, failure handling, and response consistency
- Add useful logging, monitoring, and operational diagnostics
- Use Git-based development, code review, and CI/CD practices
- Maintain implementation notes, test evidence, and runbooks for security review and operational handoff
Skills
- U.S. citizenship is required by the federal engagement supporting this position
- Applicants must reside in and perform all work from within the United States
- Two or more years of relevant software, data, or machine-learning engineering experience, including at least one year of hands-on Google Cloud work
- Strong practical Python skills, including APIs, testing, error handling, and data processing
- Hands-on experience personally building a working LLM-enabled or RAG application with Gemini on Vertex AI beyond prompt-only experimentation
- Experience deploying a containerized application or service on Google Cloud
- Familiarity with Git, REST APIs, and Docker
- A current Google Cloud Associate Cloud Engineer, Professional Cloud Developer, or Professional Machine Learning Engineer certification
- Ability to explain your personal technical contributions clearly and maintain useful documentation
- • Experience with Gemini on Vertex AI, Vertex AI Search, or Vertex AI Vector Search
- • Experience with Cloud Run, Cloud Storage, or BigQuery
- • Familiarity with embeddings, retrieval evaluation, and LLM response-quality testing
- • Familiarity with Google Agent Development Kit or another agent orchestration framework
- • Experience with GitHub Actions, Cloud Build, or another CI/CD platform
- • Experience delivering software in a federal, public-sector, or regulated environment
- • Google Cloud Professional Cloud Developer or Professional Machine Learning Engineer certification
Company Overview