Principal Software Engineer, Platform Services (PMTS)

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Job Category

Software Engineering

Job Details

About Salesforce

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.

Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

The Experience

Design, build, and maintain the backend services, APIs, data-platform automation, and AI platform / agent systems behind our data products. This is a backend-heavy role: distributed Python services, streaming. LLM/agent runtimes on AWS, RAG pipelines, and the dbt/Airflow automation that powers a Snowflake-based lakehouse.

You will work inside the Developer Experience team, whose mandate is to build the tooling, “paved paths,” and automation that let internal data teams ship discoverable, governed, and consumable data products quickly and safely.

About the platform you’ll work on

• Lakehouse & warehouse — Snowflake with Apache Iceberg tables in a medallion architecture (Bronze → Silver → Gold → Semantic Views).
• Transformation — dbt (Cloud on Fusion) with data contracts, tag-driven governance, and automated project-evaluation checks.
• Orchestration — Airflow on AWS MWAA; DAGs and schedules authored and deployed through platform tooling and CI (metadata driven).
• Developer tooling — an internal CLI and reusable utilities (dbt, Iceberg operations, Snowflake load, REST API based ingestions, etc.) that abstract the platform for data teams.
• Data quality & governance — Monte Carlo for data observability (monitors as code) and centralized governance (tagging, lineage, PII classification, cataloging).
• AI layer — Agent/MCP/Skill deployment framework, RAG, semantic router / broker agents, over our data products and catalog metadata; conversational agents in Slack; model access via AWS Bedrock / AgentCore, centralized LLM Gateway, and Snowflake Cortex.
• Cloud & delivery — AWS (ECS Fargate, Lambda, API Gateway, DynamoDB, S3, IAM, etc.), Terraform IaC, GitHub Actions CI/CD, containerized builds, structured logging, metrics, and tracing.

What You'll Actually Be Doing

• Backend servicesDesign, build, and maintain backend services in Python / Go to support our Data Platform Services — Agent runtimes on AWS Bedrock AgentCore / ECS Fargate, and event-driven Lambda handlers for our AI Platform ServicesDesign and maintain our abstraction layers for for our data domain customers via custom developed utilitiesMaintain and service our infrastructure using IaC (Terraform) for our AWS accounts
• AI / agentsBuild RAG pipelines and tool-calling AI agents for our data products: retrieval, orchestration, grounding/citations, and evaluation.Integrate model providers with our LLM Gateway with response streaming, prompt caching, and structured outputs in an auditable way (OTeL)Stand up eval harnesses, guardrails, and tracing so agent quality, latency, and cost are measurable and regressions are caught before release.
• Data-platform automationBuild and maintain data-platform automation: dbt services, MWAA/Airflow orchestration, and tooling that makes data products discoverable, governed, and consumable.Extend the platform CLI and shared utilities that data teams use as their day-to-day interface to the platform.
• Identity & securityImplement auth and identity: OAuth/OIDC flows (per-user 3LO, token vaulting, session binding), least-privilege IAM, and secrets management.Enforce multi-tenant isolation and per-user identity/RBAC across services and agents.
• Reliability & deliveryOwn service reliability and delivery: Terraform, GitHub Actions CI/CD, container builds, structured logging, metrics/tracing, alerting, and cost controls.Set technical direction: system and API design, code review, and mentoring.

You're Our Person If...

• Backend engineering15+ years designing, building, and maintaining production backend services at scaleExpert-level Python / Go / Java for server-side development; solid grasp of relevant frameworks and the WSGI/ASGI model.Service & API design: REST (and/or gRPC), request/response and streaming patterns, pagination, versioning, idempotency, and backward-compatible contracts.Data layer: SQL and data modeling, query optimization and indexing, transactions, connection pooling; relational, warehouse (Snowflake), and NoSQL/key-value (DynamoDB) stores.Server-side patterns: caching strategies, background jobs/workers, queues and event-driven processing, rate limiting, retries/backoff, and timeouts.Performance & reliability: profiling, load handling, latency/throughput trade-offs, graceful degradation, and designing for failure.Observability: structured logging, metrics, distributed tracing, and debugging live production issues.
• Software engineering fundamentalsOOP (required): encapsulation, abstraction, inheritance, composition, polymorphism; SOLID principles; design patterns applied pragmatically; strong domain modeling.Solid data structures & algorithms; ability to reason about time/space complexity.Concurrency & async programming (async/await, threading, event loops) and their failure modes.Testing (unit, integration, end-to-end) and testable design; Git and PR-based workflows; disciplined code review.
• Cloud & infrastructureProduction AWS: ECS/containers, Lambda, IAM, API Gateway, DynamoDB.Infrastructure as code with Terraform; CI/CD (GitHub Actions or equivalent) and container builds.
• Distributed systemsBuilding services that are horizontally scalable, resilient, and loosely coupled; handling consistency, retries, idempotency, and partial failure.
• SecurityOAuth/OIDC, authn/authz, token handling, least-privilege access, multi-tenant isolation, secrets management.
• AI / LLM engineeringBuilding LLM applications in production (not research).RAG: chunking, embeddings, vector search, hybrid search, reranking, grounding/citations, context-window management, retrieval evaluation.Agents: prompt engineering, tool use/function calling, structured outputs, single- and multi-step orchestration, prompt caching.Integration with model providers — Anthropic/Claude, AWS Bedrock/AgentCore, Snowflake Cortex — and response streaming.AI quality & ops: eval harnesses, guardrails, tracing/observability, token/latency/cost optimization.AI security: prompt injection, data exfiltration, PII handling, per-user identity/RBAC enforcement.

Even Better If...

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

Unleash Your Potential

When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best , and our AI agents accelerate your impact so you can do your best . Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.

Accommodations

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