u/darkspy-

I'm seeing inconsistent outputs from Gemini 2.5 Flash between my local environment and a Cloud Run deployment.

Environment:
- Vertex AI
- Gemini 2.5 Flash
- google-genai SDK 2.8.0
- Python 3.x
- Dockerized Cloud Run deployment

I've verified the following are identical:
- Source code
- Prompt
- System instruction
- Input image/text
- GenerationConfig
- Model name
- Temperature
- top_p
- top_k
- max_output_tokens

The model returns noticeably different outputs (not just wording differences—the extracted values can differ).

Things I've already checked:
- Same SDK version
- Same Docker image
- Same parameters
- Same input file
- Same project

Has anyone experienced this with Vertex AI/Gemini?

Could this be due to:
- Non-determinism even with temperature=0?
- Something else I'm missing?

Any debugging suggestions would be greatly appreciated.

I am currently working as a Machine Learning Engineer with around 2.5 years of experience.

My current experience includes:
LLM applications and prompt engineering
Document AI and information extraction
OCR pipelines
Computer Vision
VLMs (Vision Language Models)
FastAPI and Python
GCP deployment and cloud services
Production ML systems

Technologies I have worked with:
Gemini
LayoutLM
YOLO
OpenCV
FastAPI
GCP
Vector Databases
RAG systems

Most of my work has been around document understanding, extraction, classification, and automation.
My goal is to move into a Senior ML Engineer role within the next 2-3 months. I want to build 1 strong projects that demonstrate:
Production ML engineering
LLM/VLM expertise
MLOps
System design
Scalability
Research and implementation skills

I am not looking for beginner projects such as sentiment analysis, chatbots, image classifiers, etc.

Given my background, what projects would impress hiring managers at top product companies or AI startups?

If possible, please suggest projects that:
Solve real business problems
Can be open sourced
Demonstrate senior-level engineering decisions
Include deployment and monitoring aspects

Would appreciate examples from projects you’ve seen get candidates interviews or offers.