# Machine Learning Engineer (with Vertex AI Experience) at Tiger Analytics

> Remote · Canada · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/workable%3Atiger-analytics%3A26CBF6E8A3 (read 2026-11-05).

## Facts (observed)

- Company: [Tiger Analytics](https://alderhq.app/companies/tiger-analytics)
- Location text: Canada
- Where it can be done from (inferred): Remote · Canada (evidence: "Location: Canada")
- Posted: 2025-11-20 (351 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://apply.workable.com/j/26CBF6E8A3
- Skills listed (inferred): Python, FastAPI, GCP, PyTorch, TensorFlow, LLMs, Distributed systems

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (Canada, n 12): p25 CAD 137k, median CAD 171k, p75 CAD 191k

## Description (the employer's text)

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

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.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/workable%3Atiger-analytics%3A26CBF6E8A3 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.