Senior Backend AI/ML Engineer

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. Education: Masters Degree