🕒 10 days ago
Design, deploy, and operate production machine learning systems for Enterprise AI, owning the full model lifecycle and partnering across teams to deliver scalable solutions with measurable business impact.
Senior Machine Learning Engineer (GCP)
📅 Aug 1, 2025
Machine LearningGenerative AIVertex AIGoogle Cloud Platform
📜 Description
- Develop, train, and optimize models using Vertex AI, including Vertex Pipelines, AutoML, and custom training.
- Build scalable pipelines for feature engineering, training, evaluation, and deployment.
- Deploy models to Vertex AI endpoints and integrate them with applications and APIs.
- Collaborate with data science, data engineering, and MLOps teams on reproducible ML workflows.
- Monitor model performance and implement alerting, retraining triggers, and drift detection.
- Apply CI/CD, governance, versioning, explainability, and security practices across the model lifecycle.
🛠️ Requirements
- Advanced generative AI experience, including graph-based hybrid RAG, multimodal agents, agent frameworks, fine-tuning, and distillation.
- Expert Python skills, including object-oriented and functional programming.
- Proficiency with TensorFlow, PyTorch, scikit-learn, pandas, NumPy, and PySpark.
- Experience writing production-grade code, testing, and performance optimization.
- Proficiency with GCP services including Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, Pub/Sub, Dataproc, and Dataflow; understanding of IAM and VPC.
- Experience building REST APIs with FastAPI or Flask and integrating models for real-time inference.
- Experience designing scalable, fault-tolerant AI systems, distributed systems, microservices, and asynchronous processing.
Full 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. 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.
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