Software Engineering SMTS

Salesforce•Indianapolis, IN

About The Position

The Agentic Selling Team within Salesforce’s Digital Experience Technology organization builds production AI agents that transform operations for its global sales organization. As Customer Zero for Agentforce, the team pioneers agent development patterns that influence product strategy and define enterprise AI standards. The Senior Software Engineer will independently deliver high-quality solutions and drive technical excellence, architecting and building agent systems with Agentforce, Agent Script, and New Graph Architecture integrated with Salesforce CRM, Data Cloud, and external platforms. The role involves developing production systems serving more than 15,000 users, implementing agent orchestration, memory and context management, retrieval-augmented generation (RAG), integrations, and content ingestion. The engineer will also guide migrations, improve observability, mentor colleagues, make technical decisions, and lead design, implementation, testing, and delivery of major features. The team follows an Agentic Agile engineering model and bi-weekly production release cycles.

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#.
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 architecture.
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, including the ability to influence technical decisions and collaborate across organizations.
Strong test-driven development experience, including unit testing, integration testing, and end-to-end automation for AI systems.
Ability to debug complex production issues, optimize system 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.
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 large-scale content processing.
Experience with observability tools such as Grafana or Datadog and monitoring production agent systems.
Contributions to technical communities or open-source projects, or experience speaking at conferences.

Responsibilities

Design and build production agent architectures using Agentforce Builder, Agent Script, and NGA for systems serving 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.
Develop and optimize RAG patterns, retrieval strategies, and prompt engineering to improve agent quality and accuracy.
Build scalable integrations with 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.
Lead platform migrations, such as Graph to Agent Script and legacy systems to NGA, while maintaining zero production downtime.
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.