Position Overview:
We are seeking a highly skilled Senior AI/ML Engineer with over 10 years of experience designing, developing, and deploying AI/ML solutions, with deep expertise in the Google Cloud Platform (GCP) ecosystem. The successful candidate will be responsible for building scalable ML pipelines, deploying models to production, and leveraging Vertex AI and related GCP services to enable advanced analytics, predictive modeling, and AI-driven solutions. This role requires strong foundations in machine learning, MLOps practices, and cloud-native development.
Key Responsibilities:
- Design, develop, and deploy machine learning models at scale using Vertex AI, AI Platform, TensorFlow, PyTorch, or Scikit-learn.
- Build and maintain end-to-end ML pipelines including data preprocessing, feature engineering, model training, validation, deployment, and monitoring.
- Collaborate with data engineers to integrate high-quality data sources from BigQuery, Dataplex, and Dataflow into ML workflows.
- Implement MLOps best practices for versioning, reproducibility, CI/CD for ML, and automated model retraining.
- Optimize models for latency, scalability, and cost-efficiency in production environments.
- Leverage Vertex AI pipelines, Feature Store, and Model Monitoring to manage the ML lifecycle.
- Collaborate with business stakeholders and data scientists to translate requirements into AI/ML solutions that drive measurable impact.
- Explore cutting-edge AI techniques (e.g., NLP, computer vision, recommendation systems, generative AI) to prototype and deliver innovative solutions.
- Document architectures, workflows, and operational processes to ensure scalability and maintainability.
Required Skills:
- Minimum of 10 years of overall experience, with at least 3 years in AI/ML engineering.
- Strong hands-on experience with Google Cloud AI/ML services, including Vertex AI, BigQuery ML, AI Platform, and Dataplex.
- Expertise in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).
- Deep understanding of machine learning algorithms (supervised, unsupervised, deep learning, reinforcement learning).
- Experience deploying ML models as APIs or microservices using Cloud Run, Cloud Functions, or Kubernetes (GKE).
- Familiarity with feature engineering, data pipelines, and data governance in GCP.
- Experience with MLOps frameworks (Kubeflow, MLflow, TFX) and CI/CD integration.
- Strong knowledge of SQL and working with data warehouses such as BigQuery.
- Familiarity with real-time/streaming data (Pub/Sub, Kafka).
Preferred Qualifications:
- Bachelor's or Master's degree in Computer Science, Data Science, or related field (or equivalent experience).
- Google Cloud Professional Machine Learning Engineer Certification strongly preferred.
- Experience with NLP frameworks (e.g., Hugging Face Transformers, spaCy) or computer vision libraries (e.g., OpenCV, Detectron2).
- Exposure to Generative AI/LLM-based solutions and integration with GCP's Generative AI Studio is a plus.
- Experience with AutoML and low-code/no-code ML development on GCP.
