Software Engineering SMTS

Salesforce•Indianapolis, IN

About The Position

Salesforce’s Agentic Selling Team within Digital Experience Technology builds production AI agents to transform the company’s global sales organization. As Customer Zero for Agentforce, the team pioneers agent-development patterns that influence product strategy and enterprise AI standards. The Senior Software Engineer will independently deliver high-quality solutions and drive technical excellence, architecting and building sophisticated agent systems with Agentforce, Agent Script, and New Graph Architecture, integrated with Salesforce CRM, Data Cloud, and external platforms. The systems serve more than 15,000 users and support complex sales use cases. The role includes technical leadership across design, implementation, testing, deployment, and ongoing operation of agent systems. The engineer will make decisions on architecture, performance, and scalability; mentor junior engineers; participate in roadmap and architecture planning; and lead UAT and production deployments. The team uses an Agentic Agile engineering model and bi-weekly release cycles, with a focus on high availability.

Requirements

At least 6 years of software engineering development experience, with demonstrated growth in technical complexity and scope.
Strong backend development experience in Java, Python, or an object-oriented compiled, statically typed language such as C++ or C#.
Deep knowledge of AI/ML frameworks and hands-on experience with large language models, such as OpenAI, Anthropic, Claude, Gemini, or Llama.
Experience with prompt engineering, RAG architectures, and production LLM-based applications or agents.
Experience with conversational AI, chatbots, or agent-development platforms.
Experience with cloud infrastructure such as AWS, GCP, or Heroku and scalable, distributed system architectures.
Proficiency in data processing, vector databases, embeddings, semantic search, and retrieval optimization.
Strong understanding of the Salesforce platform, including Apex, LWC, Data Cloud, and Platform Events, or equivalent enterprise-platform experience.
Experience with RESTful APIs, GraphQL, event-driven architectures, and microservices patterns.
Bachelor’s or master’s degree in Computer Science, Software Engineering, or a related STEM field, with strong competencies in algorithms and data structures.
Excellent verbal and written communication skills and ability to influence technical decisions and collaborate across organizations.
Strong experience with test-driven development, including unit, integration, and end-to-end automation for AI systems.
Proven ability to debug complex production issues, optimize performance, and resolve customer-impacting problems.
Experience with developer tools such as Git, Docker, Kubernetes, Terraform, and CI/CD pipelines such as Jenkins, GitHub Actions, or Spinnaker.
Track record of mentoring engineers and elevating team technical capabilities.
Experience delivering large-scale features used by thousands of users with measurable business impact.

Nice To Haves

Hands-on experience building agents with Salesforce Agentforce or Agent Builder.
Experience with agent orchestration frameworks, multi-agent systems, and agentic AI architectures.
Deep expertise in prompt optimization, LLM fine-tuning, and model evaluation techniques.
Experience with Customer Zero or early-adopter programs that influence product development.
Knowledge of data engineering, ETL pipelines, and content processing at scale.
Experience with observability tools such as Grafana or Datadog and monitoring production agent systems.
Contributions to technical communities or open-source projects, or conference speaking.

Responsibilities

Design and build production agent architectures using Agentforce Builder, Agent Script, and New Graph Architecture for systems serving more than 15,000 users.
Architect multi-node agent orchestration, sub-agent patterns, and agentic workflows for complex sales use cases.
Implement agent memory systems, context management, and conversation-state handling for personalized experiences.
Develop and optimize RAG patterns, retrieval strategies, and prompt-engineering techniques to improve agent quality and accuracy.
Build scalable integrations connecting agents to Salesforce Data Cloud, Einstein, MuleSoft, external APIs, and MCP.
Design and implement content-ingestion pipelines for structured and unstructured data, including PDFs, web content, and documents.
Drive platform migrations, such as Graph to Agent Script and legacy systems to NGA, while maintaining zero downtime for production systems.
Implement observability, monitoring, and debugging capabilities to track conversation quality and user satisfaction.
Mentor junior engineers through code reviews, technical guidance, and knowledge sharing.
Make technical decisions about agent-design patterns, performance optimization, and scalability.
Lead design discussions, implementation, and delivery of major features in an Agentic Agile engineering model.
Participate in architecture reviews and contribute to technical roadmap planning.
Lead UAT efforts and production deployments, and maintain high availability through bi-weekly release cycles.
Debug complex production issues, optimize system performance, and resolve customer-impacting problems.