ML Engineer: Vertex AI Architect on GCP Pipelines

Aimlroles; United States; Remote; USD 150,000 - 190,000; Full time

Job summary

Tiger Analytics is seeking a skilled Machine Learning Engineer to design, build, and deploy scalable ML solutions on Google Cloud Platform, with Vertex AI as the core platform. You will operationalize models, manage the end-to-end lifecycle from data ingestion to serving and monitoring, and collaborate across cross-functional teams to ensure reliable ML workflows.

Ideal candidates have hands-on Vertex AI experience, strong Python expertise, and a deep understanding of ML systems, with a focus on

Qualifications

• Advanced knowledge of generative AI techniques and retrieval systems.
• Proficient in Python and ML/DL libraries.
• Hands-on experience with production-grade ML models and pipelines.

Responsibilities

• Develop, train, and optimize ML models using Vertex AI, including Vertex Pipelines, AutoML, and custom 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 ensure reproducible ML workflows.
• Monitor model performance and set up alerting, retraining triggers, and drift detection.

Skills

Advanced Generative AI

Graph based retrieval

Multimodal agent

Langchain Agentic Frameworks

Fine tuning

Distillation

Python

TensorFlow

PyTorch

scikit-learn

pandas

NumPy

PySpark

RESTful APIs

FastAPI/Flask

Distributed systems

Microservices

Asynchronous processing

Vertex AI

BigQuery

Cloud Storage

Cloud Run

Cloud Functions

Pub/Sub

Dataproc

Dataflow

IAM

VPC

Tools

TensorFlow

PyTorch

scikit-learn

Job description

Tiger Analytics is seeking a skilled Machine Learning Engineer to design, build, and deploy scalable ML solutions on Google Cloud Platform, with Vertex AI as the core platform. You will operationalize models, manage the end-to-end lifecycle from data ingestion to serving and monitoring, and collaborate across cross-functional teams to ensure reliable ML workflows.

Ideal candidates have hands-on Vertex AI experience, strong Python expertise, and a deep understanding of ML systems, with a focus on