Remote Machine Learning Engineer (with Vertex AI Experience)

Description:

• Tiger Analytics is seeking 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.
• The role involves operationalizing machine learning models and managing the end-to-end ML lifecycle, from data ingestion to model serving and monitoring.
• Key responsibilities include developing, training, and optimizing ML models using Vertex AI, designing and building scalable ML pipelines, and deploying models to production using Vertex AI endpoints.
• The engineer will collaborate with data scientists, data engineers, and MLOps teams to ensure reproducible and reliable ML workflows.
• Monitoring model performance and setting up alerting, retraining triggers, and drift detection mechanisms are also essential tasks.
• The role requires utilizing GCP services such as BigQuery, Dataflow, Cloud Functions, Pub/Sub, and GCS in ML workflows.
• CI/CD principles will be applied to ML models using Vertex AI Pipelines, Cloud Build, and GitOps practices.
• The engineer will implement model governance, versioning, explainability, and security best practices within Vertex AI.
• Documentation of architecture decisions, workflows, and model lifecycle for internal stakeholders is necessary.

Requirements:

• Candidates must have advanced knowledge in Generative AI, including advanced RAG and multimodal agents, as well as deep knowledge of ADK and Langchain Agentic Frameworks.
• Expertise in Python is required, with strong OOP and functional programming skills, and proficiency in ML/DL libraries such as TensorFlow, PyTorch, scikit-learn, pandas, NumPy, and PySpark.
• Experience with production-grade code, testing, and performance optimization is essential.
• Proficiency in GCP services, including Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, Pub/Sub, Dataproc, and Dataflow, is necessary, along with an understanding of IAM and VPC.
• Candidates should have experience in designing and building RESTful APIs using FastAPI or Flask, integrating ML models into APIs for real-time inference, and implementing authentication, logging, and performance optimization.
• The role requires skills in designing end-to-end AI systems with scalability and fault tolerance, as well as hands-on experience in developing distributed systems, microservices, and asynchronous processing.

Benefits:

• The position offers significant career development opportunities as the company grows.
• It provides a unique chance to be part of a small, fast-growing, challenging, and entrepreneurial environment, with a high degree of individual responsibility.