Machine Learning Engineer (with Vertex AI Experience)

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 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. 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. 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. 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
4. 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
5. 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

Benefits

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast‑growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.