Machine Learning Engineer

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.

Additional expertise required

• Advanced Generative AI, including RAG with Graph-based hybrid retrieval, multimodal agents, and familiarity with ADK, Langchain Agentic Frameworks.
• Deep knowledge of fine-tuning and distillation techniques.

Technical skills

• Expertise in Python, with strong OOP and functional programming skills.
• Proficiency with ML/DL libraries such as TensorFlow, PyTorch, scikit-learn, pandas, NumPy, and PySpark.
• Experience with production-grade code, testing, and performance optimization.

GCP Cloud Architecture & Services

• Proficiency in services like Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, Pub/Sub, Dataproc, Dataflow.
• Understanding of IAM, VPC configurations.

API Development & Integration

• Design and build RESTful APIs using FastAPI or Flask.
• Integrate ML models into APIs for real-time inference with authentication, logging, and performance tuning.

System Design & Scalability

• Design scalable, fault-tolerant AI systems, with experience in distributed systems, microservices, and asynchronous processing.

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.