Companies analyzing hour-long videos, 1,000-page PDF bundles, or entire Git repositories in a single prompt frequently search to hire a freelance Google Gemini 2.5 Pro, Gemini Flash, Context Caching & Vertex AI programmer. I am Aldian Fazrihady, a Google Cloud Certified Professional Cloud Architect, Professional Data Engineer, and Senior Freelance AI Application Engineer available via Upwork.
Core Google Gemini & Vertex AI Engineering Capabilities
- 1M–2M Token Long-Context & Context Caching Architecture: Caching massive document corpora, legal discovery bundles, or codebases in Vertex AI Context Caching for 75%+ lower repeated query cost.
- Native Video, Audio & PDF Multimodal Understanding: Extracting timestamped events, spatial bounding boxes, and structured JSON tables directly from raw MP4 video, audio recordings, and scanned engineering drawings without brittle multi-stage pipelines.
- Search Grounding & Google ADK Agents: Grounding enterprise responses with live Google Search citations and Vertex AI Search datastores.
Frequently Asked Questions
When should we use Gemini 2.5 Pro long-context caching versus vector RAG?
When users repeatedly query a shared 200k–1M token corpus (such as a company policy manual, clinical trial protocol, or construction spec book), Gemini Context Caching eliminates chunk-boundary loss and sees the entire document simultaneously.
How can we hire you for Google Gemini & Vertex AI engineering?
Contact me on Upwork.