JOB SUMMARY:
Design and implement scalable AI/ML solutions using Python and Databricks, ensuring high performance and operational efficiency. Develop cloud-native AI applications on Google Cloud using Cloud Run, AlloyDB, and API Gateway. Build secure and scalable backend APIs supporting AI-driven business capabilities. Integrate Large Language Models (LLMs), including Google's Gemini API, into enterprise applications. Implement DevOps best practices, including CI/CD automation and artifact management using JFrog Artifactory. Lead technical architecture discussions and design reviews while mentoring engineering teams. Build and maintain microservices-based applications using Docker and Kubernetes. Collaborate with Product, Data Science, Infrastructure, and Engineering teams to deliver AI-powered solutions.

Key Responsibilities
• Design and implement scalable AI/ML solutions using Python and Databricks, ensuring high performance and operational efficiency.
• Develop cloud-native AI applications on Google Cloud using Cloud Run, AlloyDB, and API Gateway.
• Build secure and scalable backend APIs supporting AI-driven business capabilities.
• Integrate Large Language Models (LLMs), including Google's Gemini API, into enterprise applications.
• Implement DevOps best practices, including CI/CD automation and artifact management using JFrog Artifactory.
• Lead technical architecture discussions and design reviews while mentoring engineering teams.
• Build and maintain microservices-based applications using Docker and Kubernetes.
• Collaborate with Product, Data Science, Infrastructure, and Engineering teams to deliver AI-powered solutions.

Required Qualifications
• 8+ years of software development experience, including at least 2 years building cloud-native applications.
• Advanced proficiency in Python with experience developing production AI/ML applications.
• Strong experience with Google Cloud Platform, including Cloud Run, AlloyDB, and API Gateway.
• Proven expertise with Databricks for data engineering and machine learning workloads.
• Experience developing applications using LLMs, including Gemini or similar AI platforms.
• Experience with JFrog Artifactory, CI/CD pipelines, and modern DevOps practices.
• Expertise in Docker, Kubernetes, and microservices architecture.
• Demonstrated technical leadership, mentoring, and architectural decision-making skills.
• Strong analytical and problem-solving abilities.