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Tiger Analytics

Senior Machine Learning Engineer (GCP)

Canada

Full-time

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Job details

Job type: Full-time
Posting language: English
Posted: Oct 4, 2026

Original 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

Advanced Generative AI
Advanced RAG including Graph based hybrid retrieval
Multimodal agent
Deep knowledge on ADK , Langchain Agentic Frameworks
Fine tuning and Distillation
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
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
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
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.

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More about this job

Responsibilities

Develop, optimize, and deploy scalable machine learning solutions using Vertex AI and GCP services, covering the full lifecycle from data ingestion and pipeline development to production serving and monitoring. Collaborate across data and MLOps teams to establish reliable workflows, CI/CD, governance, security, performance monitoring, and clear technical documentation.

Requirements

Requires advanced expertise in generative AI, RAG, multimodal and agentic frameworks, fine-tuning, distillation, and Python, along with experience in machine learning libraries and production-grade software. Candidates should be proficient in GCP architecture and services, API development, and designing scalable, fault-tolerant systems using distributed, microservice, and asynchronous processing patterns.

Skills

Generative AI
Retrieval-Augmented Generation
Graph-Based Hybrid Retrieval
Multimodal Agents
Python
Machine Learning
Vertex AI
GCP
TensorFlow
PyTorch
Scikit-Learn
PySpark
RESTful APIs
FastAPI
MLOps
Distributed Systems

Visa sponsorship

Not detected in the job text

Keywords

Generative AI
Retrieval-Augmented Generation
Graph-Based Hybrid Retrieval
Multimodal Agents
Agent Development Kit
LangChain
Fine-Tuning
Knowledge Distillation
Python
Object-Oriented Programming
Functional Programming
TensorFlow
PyTorch
Scikit-Learn
Pandas
NumPy
PySpark
Google Cloud Platform
Vertex AI
Vertex AI Pipelines
AutoML
BigQuery
Cloud Storage
Cloud Run
Cloud Functions
Pub/Sub
Dataproc
Dataflow
FastAPI
Flask

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