Principal AI Engineer
Salesforce · Mexico - Mexico City ·
- Seniority
- Principal
- Category
- AI engineering
- Experience
- 9+ years
Salesforce · Mexico - Mexico City ·
To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.
Job Category
Software EngineeringJob 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.
We are seeking a highly skilled AI Platform Engineer to play a pivotal role in building the next generation of our ML/AI platform that doesn't just support ML models, but powers autonomous AI agents at enterprise scale. This role sits at the intersection of platform infrastructure and agent systems engineering. You'll build and maintain the core infrastructure, CI/CD pipelines, and platform services that underpin our machine learning initiatives and go further in designing the harnesses, sandboxes, and evaluation frameworks that let AI agents be developed, tested, and trusted in production.
You'll work on systems that directly impact marketing, sales, service, and product growth verticals across the organization.
This isn't a traditional infrastructure role. You should be comfortable wearing multiple hats of software engineering, agent systems design, and evaluation tooling. We're looking for engineers who think in flywheels: build →evaluate → improve → ship → repeat.
Agent Harness & Flywheel Engineering
Design and build agent harness infrastructure: the scaffolding that wraps LLM calls, manages tool use, handles retries, enforces policy, and feeds results back into iterative improvement loops.
Implement agentic loop patterns with multi-turn reasoning, tool orchestration, memory management, and structured output handling as reusable platform primitives
Build the agent flywheel: automated pipelines that collect agent traces, surface regressions, route failures to evaluation, and close the loop from production signal back to prompt/model improvement
Own the end-to-end lifecycle from agent experiment to production deployment, including versioning, rollout controls, and rollback mechanisms
Sandboxing & Safe Execution
Build sandboxed execution environments for agent tools with isolating code execution, API calls, and file system access so agents can act without unconstrained blast radius
Design tiered autonomy models: define which actions agents can take automatically, which require human approval, and which are off-limits and enforced at the infrastructure layer
Implement replay and dry-run capabilities so new agent versions can be tested against real traces before going live
Agent Evaluation, Observability & Optimization
Implement evaluation frameworks for agent behavior using a combination of vendor , open source or in house built tools — covering task success, tool selection accuracy, trajectory evaluation, hallucination rates, latency, and cost
Build and maintain eval datasets, golden trace libraries, and regression test suites that run automatically on every agent code change
Instrument agent traces end-to-end: LLM calls, tool invocations, intermediate reasoning, final outputs — surfaced in Grafana or equivalent observability tooling
Define and track agent quality metrics over time; own the signal that tells the team whether agents are getting better or worse
Drive continuous quality, latency, and cost improvements across deployed agents by closing the loop between production traces, evaluations, and agent design. Optimization may be done through a variety of techniques e.g. prompt tuning, tool calling optimizations, context engineering, right-sizing model selection per task and explore distillation or fine-tuning (SFT, DPO, RLHF) on curated trace data to name a few
Validate every optimization through A/B tests, shadow deployments, and replay against golden traces, with the eval suite gating rollout so wins are real and regressions are caught before they reach users
CI/CD & Workflow Automation
Build and optimize CI/CD pipelines (GitHub Actions, ArgoCD) that cover not just code deployment but agent evaluation gates — no agent ships without passing its eval suite
Automate Docker and package builds, security scanning, and agent integration tests as first-class pipeline steps
Design self-healing CI patterns where agent-based automation can diagnose and fix common pipeline failures
Tooling, Developer Experience & Architecture
Build internal tools and developer self-service interfaces that let ML engineers and data scientists iterate on agents without platform team involvement
Maintain a comprehensive view of how all platform components -> infrastructure, agent harnesses, evaluation pipelines, observability — work together
Create architecture diagrams and drive long-term platform vision; own the "how does this scale to 10x" conversation
Monitoring, Security & Reliability
Establish alerting (Grafana, PagerDuty) for both traditional platform health and agent-specific signals (error rates, tool call failures, eval score drift)
Ensure all agent infrastructure adheres to security best practices: sandboxed execution, auditable traces, access controls on every tool
Participate in security reviews; own compliance for agent workloads
9+ years as a Platform Engineer, ML Infrastructure Engineer, or Software Engineer
Demonstrated experience building agent harness infrastructure using agentic loops, tool orchestration, structured output handling, multi-turn conversation management
Hands-on experience with agent evaluation frameworks like Braintrust, LangSmith, or equivalent , including building eval datasets, running automated regression suites, and tracking quality metrics over time
Strong understanding of sandboxing and safe agent execution like isolation patterns, tiered autonomy, blast radius controls
Experience with context Engineering as it relates to Agent orchestration.
Strong Python engineering skills for building scalable tools, automation, and platform components
Deep expertise in AWS
Extensive experience with CI/CD tooling, especially GitHub Actions and ArgoCD
Proficiency in infrastructure-as-code (Terraform)
Experience with containerization (Docker) and orchestration (Kubernetes)
Experience with AgentOps concepts and production Multi Agent systems
Strong problem-solving skills and ability to manage multiple priorities across a complex platform
Preferred Qualifications (Bonus Points):
Experience with Salesforce Ecosystem including Agentforce and Data360
Experience with unstructured databases(vector or graph databases) and RAG pipelines
Experience working with modern data platforms and real-time processing frameworks, including cloud data warehouses (e.g., snowflake), streaming technologies (e.g. kafka, flink)
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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