Senior Machine Learning Engineer (GCP)

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.