• Design and implement scalable AI/ML pipelines using Databricks and Python, optimizing for performance and cost
• Perform AI Engineering development, cloud-native solutions on Google Cloud, leveraging AlloyDB for data persistence and CloudRun for containerized services
• Build and optimize APIs using Google Cloud API Gateway, ensuring security, rate limiting, and monitoring
• Develop LLM applications leveraging Google's Gemini API for natural language understanding and generation
• Establish DevOps and artifact management practices using JFrog Artifactory for dependencies, build artifacts, and container images
• Lead technical design reviews and architectural decisions, providing technical guidance and mentorship to junior engineers
• Implement CI/CD pipelines with emphasis on build artifact management, versioning, and deployment automation
• Collaborate with cross-functional teams (Product, Data Science, Infrastructure) to deliver high-impact features

Required Qualifications

• 8+ years of software development experience with at least 3+ years in cloud-native architectures
• Advanced Python skills with demonstrated experience in production ML/AI systems
• Hands-on experience with Google Cloud Platform (GCP), specifically with AlloyDB, CloudRun, and API Gateway
• Proven expertise with Databricks for data engineering, feature engineering, and ML model training
• Strong understanding of LLM concepts and experience integrating or building applications with LLMs
• Experience with artifact repositories and build systems (JFrog Artifactory or equivalent)
• Expertise in microservices architecture, containerization (Docker/Kubernetes), and modern DevOps practices
• Demonstrated leadership skills with experience mentoring junior developers and driving technical excellence
  • Strong problem-solving skills and ability to take ownership of complex, ambiguous challenges