Agentforce AI & Cloud Backend Engineering

• Design, develop, and implement scalable AI-powered backend solutions supporting enterprise Agentforce initiatives on Google Cloud Platform.
• Develop intelligent applications leveraging Google’s Gemini API and modern AI/ML frameworks for natural language processing, automation, and generative AI use cases.
• Architect, develop, and optimize cloud-native microservices using Python, Cloud Run, and serverless technologies to deliver secure, highly available, and scalable solutions.
• Build and enhance RESTful APIs utilizing Google Cloud API Gateway while ensuring security, governance, and performance best practices.
• Develop and maintain AI/ML data processing pipelines using Databricks to support model training, inference, and enterprise analytics.
• Implement Infrastructure as Code (IaC) using Terraform and automate deployments through GitOps-based CI/CD pipelines using Azure DevOps.
• Manage build artifacts, package repositories, and release management utilizing JFrog Artifactory.
• Design and implement event-driven architectures using Google Pub/Sub and Kafka to enable scalable enterprise integrations.
• Work closely with Product Owners, Data Engineering, Infrastructure, and Architecture teams to deliver business-driven AI solutions.
• Ensure solutions adhere to Google Cloud security standards including IAM, encryption, networking, compliance, and operational excellence.
• Participate in technical solution design, architecture discussions, code reviews, and client workshops while providing technical guidance and implementation best practices.
• Support client engagements by contributing to technical estimations, solution design, and implementation planning for AI and cloud transformation initiatives.

Basic Qualifications: (What are the skills required to this job with minimum years of experience on each)

• Minimum 3-5 years of experience in Backend Engineering, AI/ML Engineering, Cloud Engineering, or Software Development.
• Minimum 3 years of hands-on experience developing applications using Python.
• Minimum 2+ years of experience designing and deploying solutions on Google Cloud Platform, including Cloud Run, GKE, BigQuery, Cloud Storage, AlloyDB, and serverless services.
• Minimum 2 years of experience developing RESTful APIs and integrating enterprise applications using Google Cloud API Gateway.
• Minimum 2 years of experience implementing AI/ML or data engineering solutions using Databricks.
• Experience implementing Infrastructure as Code using Terraform.
• Experience building CI/CD pipelines utilizing GitOps methodologies, Azure DevOps, and JFrog Artifactory.
• Experience working with messaging and event streaming technologies such as Google Pub/Sub and Kafka.
• Strong understanding of Google Cloud Platform IAM, cloud networking, encryption, security, and governance best practices.
• Excellent analytical, troubleshooting, communication, and stakeholder collaboration skills. *

Travel

This position may require up to 20-30% travel depending on client engagements, project requirements, and business needs.

Degree

Bachelor’s degree in Computer Science, Engineering, Information Technology, or equivalent work experience.

Nice to Have (But Not Required)

• Experience with Salesforce Agentforce or enterprise AI platforms.
• Experience working with Google’s Gemini models or other Generative AI technologies.
• Knowledge of MLOps frameworks and model lifecycle management.
• Experience with Kubernetes and container orchestration.
• Consulting or client-facing implementation experience.
• Technical leadership or project coordination experience.

DTS offers excellent compensation package.