AI/ML Engineer

Face to Face interview mandatry 

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.?