Remote Senior Machine Learning Engineer (GCP)

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 driving 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, deploying models to production, and collaborating with data scientists, data engineers, and MLOps teams.
• The engineer will monitor model performance, set up alerting, retraining triggers, and drift detection mechanisms, and utilize GCP services such as BigQuery, Dataflow, Cloud Functions, Pub/Sub, and GCS in ML workflows.
• The position requires applying CI/CD principles to ML models, implementing model governance, versioning, explainability, and security best practices within Vertex AI, and documenting architecture decisions and workflows clearly for internal stakeholders.
• Additional responsibilities include advanced generative AI tasks, Python expertise, GCP cloud architecture and services, API development and integration, and system design and scalability.

Requirements:

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

Benefits:

• This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment.
• The role provides a high degree of individual responsibility, allowing for personal and professional growth within the company.