Principal AI Engineer

Requirements 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 such as isolation patterns, tiered autonomy, and 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 (Desirable) Experience with the Salesforce Ecosystem including Agentforce and Data360 (Desirable) Experience with unstructured databases (vector or graph databases) and RAG pipelines (Desirable) Experience working with modern data platforms and real‑time processing frameworks, including cloud data warehouses (e.g., Snowflake) and streaming technologies (e.g., Kafka, Flink) What the job involves 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 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 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 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 such as prompt tuning, tool‑calling optimizations, context engineering, right‑sizing model selection per task and exploring 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 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 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 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 #J-18808-Ljbffr