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Senior Machine Learning Engineer (GCP) at Tiger Analytics. 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. . . . 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. Company Location: Canada.