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Salesforce

Builds cloud software for customer relationships

Principal Software Engineer - Platform Services, Payments

AI Application Engineering +1Updated on Nov 11
Full-timeIn-person

About the Job

Requirements

  • At least 15 years of experience designing, building, and maintaining production backend services at scale.
  • Expert-level Python, Go, or Java expertise for server-side development, including familiarity with relevant frameworks and the WSGI/ASGI model.
  • Experience designing services and APIs, including REST and/or gRPC, request/response and streaming patterns, pagination, versioning, idempotency, and backward-compatible contracts.
  • Knowledge of SQL and data modeling, query optimization and indexing, transactions, connection pooling, and relational, warehouse, and NoSQL/key-value stores.
  • Knowledge of server-side caching strategies, background jobs and workers, queues and event-driven processing, rate limiting, retries and backoff, and timeouts.
  • Experience with profiling, load handling, latency and throughput trade-offs, graceful degradation, and failure-resistant design.
  • Experience with structured logging, metrics, distributed tracing, and debugging live production issues.
  • Knowledge of object-oriented programming, including encapsulation, abstraction, inheritance, composition, polymorphism, SOLID principles, pragmatic design patterns, and domain modeling.
  • Knowledge of data structures and algorithms and time and space complexity.
  • Experience with concurrency and asynchronous programming, including async/await, threading, event loops, and their failure modes.
  • Experience with unit, integration, and end-to-end testing, testable design, Git, pull-request workflows, and code review.
  • Production experience with AWS, including ECS and containers, Lambda, IAM, API Gateway, and DynamoDB.
  • Experience with infrastructure as code using Terraform, CI/CD using GitHub Actions or equivalent, and container builds.
  • Experience building horizontally scalable, resilient, loosely coupled distributed services and handling consistency, retries, idempotency, and partial failure.
  • Experience with OAuth/OIDC, authentication and authorization, token handling, least-privilege access, multi-tenant isolation, and secrets management.
  • Experience building production LLM applications, including RAG chunking, embeddings, vector and hybrid search, reranking, grounding and citations, context-window management, and retrieval evaluation.
  • Experience with agents, prompt engineering, tool use and function calling, structured outputs, single- and multi-step orchestration, and prompt caching.
  • Experience integrating model providers, including Anthropic/Claude, AWS Bedrock/AgentCore, or Snowflake Cortex, including response streaming.
  • Experience with AI evaluation harnesses, guardrails, tracing and observability, token, latency, and cost optimization, and AI security concerns including prompt injection, data exfiltration, PII handling, and per-user identity/RBAC enforcement.

Responsibilities

  • Design, build, and maintain Python and Go backend services for Data Platform Services, including agent runtimes on AWS Bedrock AgentCore and ECS Fargate and event-driven Lambda handlers for AI Platform Services.
  • Design and maintain abstraction layers and custom-developed utilities for data-domain customers.
  • Maintain and service AWS account infrastructure using Terraform infrastructure as code.
  • Build RAG pipelines and tool-calling AI agents for data products, covering retrieval, orchestration, grounding and citations, and evaluation.
  • Integrate model providers with the LLM Gateway using response streaming, prompt caching, and structured outputs in an auditable way.
  • Build evaluation harnesses, guardrails, and tracing to measure agent quality, latency, and cost and catch regressions before release.
  • Build and maintain data-platform automation, including dbt services, MWAA/Airflow orchestration, and tooling that makes data products discoverable, governed, and consumable.
  • Extend the platform CLI and shared utilities used by data teams as their day-to-day platform interface.
  • Implement authentication and identity, including per-user 3LO OAuth/OIDC flows, token vaulting, session binding, least-privilege IAM, and secrets management.
  • Enforce multi-tenant isolation and per-user identity/RBAC across services and agents.
  • Own service reliability and delivery, including Terraform, GitHub Actions CI/CD, container builds, structured logging, metrics and tracing, alerting, and cost controls.
  • Set technical direction through system and API design, code review, and mentoring.

Desired Qualifications

  • Hands-on experience with dbt, Airflow/MWAA, and Snowflake.
  • Experience with MCP (Model Context Protocol) and/or applied RAG.
  • Experience with the Slack platform, including Bolt, Socket Mode, or Block Kit, or other real-time/conversational backends.
  • Experience with data observability such as Monte Carlo, or data-quality and governance tooling.
  • Experience with fine-tuning or adaptation, semantic caching, or model routing and fallback.
  • Experience adding AI capabilities to existing production systems.
  • Experience with Iceberg, open table formats, and lakehouse patterns.
  • Experience with Tableau or other business intelligence integration.

About the Company

Salesforce

Builds cloud software for customer relationships

Salesforce sells cloud software that helps businesses manage sales leads, customer service, marketing, and online commerce from shared customer records. Its flagship Sales Cloud tracks accounts and deals, while Service Cloud organizes support cases and Agentforce builds AI assistants that act on company data. Teams access these tools through a browser and connect outside apps and data sources, with Slack for work messages and Tableau for visual analysis. Salesforce earns most of its revenue from software subscriptions and support, with professional services for implementation and training. Marc Benioff co-founded Salesforce in 1999, and its headquarters are in San Francisco, California.

Company size
10,001+
Company stage
IPO
Headquarters
San Francisco, CA
Founded
1999

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