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jazzhr

AI Platform Engineer

PairSoft
Remote, IND
Full-time
Senior · 5+ years experience
Remote
Discovered 1 weeks ago
PythonGoJavaLangSmithLangfuseBraintrust
Free

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Full Job Posting

  • Design, build, and operate services in the central AI platform. Every service should have clear API contracts, versioning, and SLOs from day one.
  • Write production Python for AI services. Contribute to shared libraries, SDKs, and integration patterns that product teams will consume.
  • Instrument everything: cost tagging per request, latency and error metrics, quality signals, and audit logs. If it is not measured, it is not shipped.
  • Own on-call rotation for the AI platform services you build. Author runbooks and improve them after every incident.
  • Work directly with product engineering leads across the product lines to onboard their AI features onto the central platform.
  • Provide technical support, integration guidance, and troubleshooting to product teams consuming platform services.
  • Contribute to Architecture Decision Records. Push back on decisions you disagree with; document tradeoffs.
  • Set the operational bar: observability, alerting, incident response, and post-incident reviews.
  • Own vendor evaluation for tools in your area of specialization. Run bakeoffs when the choice is not obvious. Make cost, quality, and reliability tradeoffs explicit.
  • Contribute to the AI security posture: PII handling, tenant isolation, prompt injection defense, and audit logging within your services.
  • The AI-forward end of the platform. You build the retrieval, prompt, and guardrail systems that make LLM output good enough to ship to customers.
  • RAG-as-a-service platform: ingestion, chunking, embedding, retrieval quality, and hybrid search.
  • Prompt engineering at scale: templates, evaluation, versioning, and per-tenant customization.
  • Guardrails and content safety: input filtering, output validation, PII redaction, tool-use sandboxing.
  • Agent frameworks and tool-use patterns as agent workflows move into production across product lines.
  • Domain-specific fine-tuning experiments and quality benchmarking.
  • Multi-provider model gateway with routing, fallback, retry, and rate-limit logic.
  • Prompt registry, versioning, and rollout controls (canary, feature flags).
  • Shared libraries and SDKs for product-team consumption. API contracts, versioning, deprecation strategy.
  • Tenant isolation architecture: how customer data flows through platform services safely.
  • Cost attribution and budget enforcement at the gateway layer.
  • Observability platform: prompt and response tracing, cost per request, quality signals, drift detection.
  • Evaluation infrastructure: golden datasets, offline evals, LLM-as-judge patterns, regression testing.
  • Model deployment pipelines, including fine-tuned models where applicable.
  • Alerting and SLO framework for AI services. Distinct from general engineering SLOs: quality regression is a first-class alert.
  • Fine-tuning and RLHF pipelines when product-specific tuning becomes justified.
  • Bachelor's or Master's degree in Computer Science, Engineering, or equivalent.
  • 5+ years of professional software engineering experience with a strong production track record.
  • 6+ years building production distributed systems, ideally including internal developer platforms or API gateways at scale.
  • 5+ years in MLOps, LLMOps, ML platform engineering, or a hybrid DevOps plus ML role at production scale.
  • Hands on experience with observability tools for LLM systems: LangSmith, Langfuse, Braintrust, Arize, or comparable.
  • Working knowledge of evaluation methodology for LLM systems: benchmark design, LLM-as-judge, human review workflows
  • Working fluency in the modern LLM ecosystem: OpenAI or Anthropic APIs, at least one orchestration framework (LangChain, LlamaIndex, or equivalent), at least one vector database, at least one observability tool.
  • 2+ years of hands-on production experience with LLM-based systems: prompt engineering, RAG, evaluation, or LLM infrastructure.
  • Strong Python and one of Go or Java. Comfortable with async patterns, backpressure, and rate limiting. Comfortable writing production code, not just notebooks or scripts.
  • Experience designing multi-tenant systems with hard isolation guarantees.
  • Cloud-native depth on Azure or AWS: Kubernetes, service mesh, IaC (Terraform), CI/CD.
  • Experience shipping model updates safely in production: canaries, shadow evaluation, rollback triggers.
  • Comfort with the full ML lifecycle: training pipelines, serving infra, monitoring, and cost management.
  • Strong grasp of AI security fundamentals: PII handling, tenant isolation, prompt injection basics.
  • Ability to communicate technical decisions clearly in async writing. This role is distributed across time zones and cannot be run on synchronous meetings alone.
  • Fluent English language skills
  • Domain experience in procure-to-pay, ERP integration, accounts payable, procurement, or adjacent finance and operations software.
  • Experience at a product company or PE-backed B2B SaaS, ideally on an internal platform team.
  • Contributions to open-source AI/ML infrastructure projects.
  • Experience with agent frameworks (LangGraph, AutoGen, CrewAI, or custom orchestration) in production.
  • Prior experience on a founding platform team where you shipped v1 of a service used by multiple internal customers.

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