Overview
This role focuses on building and maintaining machine learning infrastructure and data pipelines on Google Cloud Platform (GCP). The position requires experience in deploying and monitoring production ML models, managing large-scale datasets (with emphasis on unstructured text), and collaborating closely with data science teams to enable scalable, reliable, and observable ML applications.
Responsibilities
- Pipeline Development: Design and implement ML pipelines for ingestion, transformation, inference, and storage of structured and unstructured data
- Cloud & Platform Expertise: Utilize GCP services (Vertex AI, BigQuery, Dataflow, Kubeflow, CloudRun) to enable model training, deployment, and monitoring
- Model Deployment: Build and manage APIs and microservices for real-time and batch inference, including version control, rollback strategies, and CI/CD integration
- Data Processing: Develop ETL processes, perform validation, and automate workflows for both structured and text-heavy datasets
- Text Data Management: Implement embeddings, chunking strategies, and NLP/LLM tools to support unstructured data applications
- Database & Storage: Manage SQL, NoSQL, feature stores, and vector databases for ML workloads
- Monitoring & Optimization: Use GCP monitoring tools to track performance, latency, prediction drift, feature drift, and data quality; build dashboards for real-time observability
- Collaboration: Work closely with data science teams to bridge research and production systems
- Best Practices: Contribute to automation, documentation, cost-efficiency, and reproducibility standards
Required Qualifications
- Minimum 3 years in ML engineering, MLOps, or data engineering
- Bachelor's degree in a STEM discipline from a top-tier institution
- Strong proficiency with Google Cloud Platform (GCP), including Vertex AI
- Experience with big data frameworks (BigQuery, Dataflow, Spark optional)
- Hands-on experience with workflow orchestration (Kubeflow, Airflow, Cloud Composer)
- Proven experience deploying ML models in production at scale
- Proficiency in Python and SQL
- Expertise in GCP's AI and data ecosystem (Vertex AI, BigQuery, Dataflow, Kubeflow, CloudRun)
- Experience with ETL and data integration pipelines
- Familiarity with feature stores and vector databases
- Knowledge of containerized deployment (e.g., CloudRun, Docker, Kubernetes)
- Experience handling large-scale text datasets
- Prior collaboration with machine learning or modeling teams
Preferred Qualifications
- Experience with NLP libraries (NLTK, SpaCy, HuggingFace Transformers, Regex)
- Familiarity with LLM ecosystem tools (LangChain, LlamaIndex, Instructor, embeddings)
- Exposure to LLM APIs (OpenAI, Anthropic, etc.)
- Experience with monitoring frameworks and drift detection methods
Why Join This Team
This position offers the opportunity to work on advanced ML infrastructure in a high-performing environment, applying modern cloud technologies to real-world large-scale data challenges.
Seniority level
Employment type
Job function
- Information Technology and Engineering
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