Cloud Engineer with AI/ML

Tasks

• Develop LLM-powered applications using Google's Gemini API for natural language understanding and generation.
• Design and optimize scalable AI/ML pipelines using Databricks and Python.
• Build cloud-native solutions on GCP, leveraging AlloyDB for data persistence and Cloud Run for containerized services.
• Develop and optimize APIs using Google Cloud API Gateway, including security, rate limiting, and monitoring.
• Establish DevOps and artifact management practices using JFrog Artifactory.
• Implement CI/CD pipelines with automated builds, artifact versioning, and deployments.
• Lead technical design reviews and architectural decisions.
• Provide technical guidance and mentorship to junior engineers.
• Collaborate with Product, Data Science, and Infrastructure teams to deliver high-impact solutions.

Requirements

• Strong experience in DevOps and Cloud Engineering
• Python programming and AI engineering
• Google Cloud Platform (GCP): GKE, Cloud Run, serverless architectures
• Databricks and scalable AI/ML pipelines
• Google Gemini API for LLM application development
• GCP data services: BigQuery, Spanner, AlloyDB, and Cloud Storage
• CI/CD, GitOps, and API development
• JFrog Artifactory for artifact and dependency management
• Terraform and Infrastructure as Code (IaC)
• Event-driven systems: Pub/Sub and Kafka
• Data ingestion pipelines
• GCP IAM, encryption, and cloud security best practices
• Azure DevOps (ADO) repository management
• Local Candidates Only

Schedule

• Onsite
• Face-to-Face (F2F) interview

How to apply

• Share profiles by email.

Location

• Atlanta, GA