Job Description: AI Platform Engineer to design and build the foundational components that power enterprise-scale GenAI applications. This includes data guardrails, model safety tooling, observability pipelines, evaluation harnesses, and standardized logging/monitoring frameworks. This role is critical for enabling safe, reliable, and compliant AI development across multiple use cases, teams, and business units. Idea is to create the common platform services that AI team will build upon.
Key Responsibilities1. Guardrails, Safety \& Governance* Design and implement data guardrail frameworks (pre-processing, redaction, PII/PHI filtering, DLP integration, prompt defenses).
Build "Model Armor" components such as: Input validation \& sanitization Prompt-injection defenses Harmful content detection \& policy enforcement Output filtering, factchecking, grounding checks
Integrate safety tooling (policy engines, classifiers, DLP APIs/safety models).
Collaborate with Security, Compliance, and Data Privacy teams to ensure frameworks meet enterprise governance requirements.
2. Observability Frameworks* Build and maintain observability pipelines using tools like Arize AI (tracing, quality metrics, dataset drift/hallucination tracking, embedding monitoring).
Define and enforce platform-wide standards for: Tracing LLM calls Token usage and cost monitoring Latency and reliability metrics Prompt/model version tracking
Provide reusable SDKs or middleware for engineering teams to adopt observability with minimal friction.
3. Logging, Monitoring \& Telemetry* Design standardized LLM-specific logging schemas, including: Inputs/outputs Model metadata Retrieval metadata Safety flags User context and attribution
Build monitoring dashboards for performance, cost, anomalies, errors, and safety events.
Implement alerting and SLOs/SLIs for LLM inference systems.
4. Evaluation Infrastructure* Architect and maintain evaluation harnesses for GenAI systems, including: RAG evaluation (faithfulness, relevance, hallucination risk) Summarization/QA evaluation Human-in-the-loop review workflows Automated eval pipelines integrated into CI/CD
Support frameworks such as RAGAS, G-Eval, rubric scoring, pairwise comparisons, and test case generation.
Build reusable tooling for teams to write, run, and track model evaluations.
5. Platform Engineering \& Reusable Components* Develop shared libraries, APIs, and services for: Prompt management/versioning Embedding pipelines and model wrappers Retrieval adapters Common data loaders and document preprocessing Tool/function schemas
Drive consistency across teams with standards, reference architectures, and best practices.
Review system designs across use cases to ensure alignment to platform patterns.
6. Collaboration \& Enablement* Partner with AI engineers, product teams, and data scientists to understand cross-cutting needs and convert them into reusable platform features.
Create documentation, onboarding guides, examples, and developer tooling.
Provide internal training (brown bags, workshops) on guardrails, observability, and evaluation frameworks.
Required Qualifications Technical Skills
5-10+ years software engineering or ML infrastructure experience.
Strong Python engineering fundamentals (FastAPI, async, typing/Pydantic, testing).
Experience with model safety/guardrails approaches (prompt injection defense, PII redaction, toxicity filters, policy enforcement).
Hands-on with Arize AI, LangSmith, or similar LLM observability platforms.
Experience creating evaluation frameworks using RAGAS, G-Eval, or custom rubric systems.
Strong familiarity with vector databases (Pinecone, Weaviate, Milvus), embeddings, and retrieval pipelines.
Solid understanding of LLM architectures, tokenization, embeddings, context limits, and RAG patterns.
Experience in cloud (Google Cloud Platform preferred), Kubernetes/GE, containers, and CI/CD.
Strong understanding of security, governance, DLP, data privacy, RBAC, and enterprise compliance requirements.
Soft Skills
Strong documentation and communication skills.
Ability to influence engineering teams and standardize best practices.
Comfortable working across multiple stakeholders-platform, security, ML engineering, product.
Nice to Have
Experience with LangChain/LangGraph or Llamalndex orchestrations.
Experience with Guardrails.ai, Rebuff, Protect AI, or similar LLM security tooling.
Experience with Google Cloud Platform Vertex AI pipelines, Model Monitoring, and Vector Search.
Familiarity with knowledge graphs, grounding models, fact-checking models.
Building SDKs or developer frameworks adopted across multiple teams.
On-prem or hybrid AI deployment experience.
For applications and inquiries, contact: hirings@openkyber.com