# Senior Machine Learning Engineer (GCP)

**Company:** [Tiger Analytics Inc.](http://jobs.workable.com/companies/sJUKvzuwkZUks5P23HYp2P.md)
**Location:** Remote
**Workplace:** remote
**Department:** MLE

[Apply for this job](http://jobs.workable.com/view/d910427e-ece5-4ca1-92a3-7071dd8fe0fe)

## Description

Tiger Analytics is looking for a skilled and innovative **Machine Learning Engineer** with hands-on experience in **Google Cloud Platform (GCP)** and **Vertex AI** to design, build, and deploy scalable ML solutions. You will play a key role in operationalizing machine learning models and driving the end-to-end ML lifecycle, from data ingestion to model serving and monitoring.

### **Key Responsibilities:**

-   Develop, train, and optimize ML models using **Vertex AI**, including Vertex Pipelines, AutoML, and custom model training.
-   Design and build scalable ML pipelines for feature engineering, training, evaluation, and deployment.
-   Deploy models to production using Vertex AI endpoints and integrate with downstream applications or APIs.
-   Collaborate with data scientists, data engineers, and MLOps teams to enable reproducible and reliable ML workflows.
-   Monitor model performance and set up alerting, retraining triggers, and drift detection mechanisms.
-   Utilize GCP services such as **BigQuery, Dataflow, Cloud Functions, Pub/Sub**, and **GCS** in ML workflows.
-   Apply CI/CD principles to ML models using **Vertex AI Pipelines**, **Cloud Build**, and **GitOps** practices.
-   Implement model governance, versioning, explainability, and security best practices within Vertex AI.
-   Document architecture decisions, workflows, and model lifecycle clearly for internal stakeholders.

## Requirements

1\. Advanced Generative AI  
    - Advanced RAG including Graph based hybrid retrieval  
    - Multimodal agent  

-   Deep knowledge on ADK , Langchain Agentic Frameworks
-   Fine tuning and Distillation   
    

2\. Python Expertise  
    - Expert in Python with strong OOP and functional programming skills  
    - Proficient in ML/DL libraries: TensorFlow, PyTorch, scikit-learn, pandas, NumPy, PySpark  
    - Experience with production-grade code, testing, and performance optimization  
   
3\. GCP Cloud Architecture & Services  
    - Proficiency in GCP services such as:  
      - Vertex AI  
      - BigQuery  
      - Cloud Storage  
      - Cloud Run  
      - Cloud Functions  
      - Pub/Sub  
      - Dataproc  
      - Dataflow  
    - Understanding of IAM, VPC  
  
6\. API Development & Integration  
    - Designs and builds RESTful APIs using FastAPI or Flask  
    - Integrates ML models into APIs for real-time inference  
    - Implements authentication, logging, and performance optimization  
   
7\. System Design & Scalability  
    - Designs end-to-end AI systems with scalability and fault tolerance in mind  
    - Hands-on experience in developing distributed systems, microservices, and asynchronous processing

## Benefits

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.