Senior Machine Learning Engineer (GCP)

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.