The Role:
We are seeking a
Senior AI Platform Engineer
to own the core platform layer that powers every AI agent in production — from multi-tenant agent configuration and schema architecture, to data pipeline contracts, evaluation harnesses, and customer onboarding automation.
This role sits at the intersection of backend platform engineering, LangGraph-based orchestration, and AI evaluation systems. You won't just build features — you'll own the infrastructure that makes all features possible: the agent orchestration graph, the customer configuration schema, end-to-end conversation logging, automated eval pipelines, and the scripts that deploy new customers in under 30 minutes.
If you love owning systems that other engineers depend on, ship at high velocity across a wide surface area, and take pride in leaving codebases cleaner than you found them — we want to hear from you.
Responsibilities:
Core Platform & Schema Architecture
Own and evolve the
core platform repository
— the central Python package implementing our modular agent architecture across orchestration, tools, state, retrieval, configuration, and extensibility layers.
Design and maintain
customer configuration schemas
including versioning metadata, lineage tracking, and component provenance fields aligned with our IP strategy.
Implement backward-compatible schema extensions and ensure all active customer deployments upgrade without breaking changes.
Enforce
schema validation
at all node inputs/outputs to prevent data drift across multi-tenant environments.
Multi-Tenancy Architecture
Build and maintain
cross-client isolation
across customer configuration, persistent state, and RAG pipelines.
Implement
multi-tenant tagging
so conversation logs, eval datasets, and agent behaviors remain cleanly separated per customer.
Design
config-driven deploy parameterization
to enable new customer onboarding without code changes — configuration-only deployment model.
Ensure all platform changes are backward compatible —
no code forking per customer
.
Data Pipelines & Conversation Logging
Own the
end-to-end conversation logging system
— unified schema, row format, conversation capture, and metadata persistence to PostgreSQL and S3.
Maintain and extend
knowledge base ingestion pipelines
: scraping, embedding, vector DB indexing, and retrieval validation for each customer deployment.
Define and freeze
data contracts
between capture specifications and implementation — so downstream analytics, fine-tuning, and eval all receive consistent, well-structured inputs.
Implement




multi-tenant data tagging
so every logged conversation is attributed to the correct customer, facility, and session.
Eval Systems & Quality Gates
Own the
eval suite
end-to-end: scenario design, ground-truth dataset curation, automated scoring (F1, precision, recall), and regression CI gates.
Build and maintain
LLM simulation test flows
— parameterized test scenarios that exercise the agent across reservations, pricing, sizing, escalation, and context retention.
Instrument
distributed tracing
at the LangGraph node level — capturing token usage, latency per node, and score drift across deployments.
Implement
eval suite parameterization
so the same harness works across all customers with minimal configuration.
Define and enforce
production-ready gates
— eval score thresholds that must be met before any agent goes live.
Onboarding Automation & Deployment
Build and maintain
onboarding automation scripts
that deploy a new customer in under 30 minutes: configuration templates, KB ingestion, eval suite setup, and run scripts.
Own
deploy parameterization
— all customer-specific values injected via config, never hardcoded.
Maintain
platform sync
across customer repositories — keeping shared platform code consistent without breaking customer-specific deployments.
Document and enforce the deployment SOP so any engineer can execute a new deployment without escalation.
Reliability & Observability
Ensure all platform APIs meet
latency targets
(P95 < 1.5s for voice path) through profiling, caching, and async optimization.
Maintain
structured logging
at every critical path node — conversation start/end, intent classification, retrieval hits, booking outcomes.
Implement
CI/CD gates
that run eval and schema validation automatically before any merge to the production branch.
Contribute to
incident diagnosis
by maintaining observable, well-logged systems with clear error paths.
Requirements:
Experience:
Must:
Proficient or Advance use of agentic workflows for coding in tools like Cursor AI or Claude Code.
4+ years building and owning production-grade backend systems in Python.
Proven experience owning a




core platform or shared infrastructure layer
used by multiple teams or customers.
Hands-on track record with
multi-tenant system design
— schema isolation, config-driven parameterization, and deployment automation.
Experience building
evaluation harnesses
for LLM-based systems with quantitative metrics.
Tools / Technologies:
Python (advanced):
async I/O, FastAPI, Pydantic, pytest, type hinting, data classes.
LangGraph:
state machines, conditional edges, node composition, shared state management across modular agent layers.
PostgreSQL + pgvector:
relational schema design, state persistence, multi-tenant data isolation.
RAG pipelines:
vector DB (Pinecone or equivalent), embedding pipelines, retrieval evaluation.
Eval & tracing frameworks:
LLM simulation testing, distributed tracing, automated scoring pipelines.
GitHub Actions / CI/CD:
automated eval gates, schema validation hooks, environment promotion.
AWS:
EC2, S3, RDS, IAM — production deployment and infrastructure operations.
YAML / config-driven deployment:
customer configuration templating, parameterized onboarding scripts.
Skills:
Strong
systems thinking
— ability to see how schema decisions in the core platform ripple downstream to eval, logging, onboarding, and customer deployments.
Comfort owning
wide surface area
— this role crosses platform, data, eval, and ops without a narrow specialization.
High
individual shipping velocity
— ability to close multiple GitHub issues per day with clean PRs and minimal back-and-forth.
Strong
schema discipline
— treats data contracts as first-class artifacts, not afterthoughts.
Ability to work
autonomously
with minimal supervision in a fast-moving startup environment.
Strong written communication for PR descriptions, Notion documentation, and deployment SOPs.
Preferred Qualifications:
Experience with
IP-aware architecture decisions
or contributing to software patent documentation.
Familiarity with
voice agent systems
(Twilio, PSTN, LiveKit) and latency-constrained deployments.
Experience with
multi-model evaluation
(comparing models from OpenAI, Anthropic, Mistral) using quantitative benchmarks.
Prior work in
self-storage, property management, or regulated verticals
where data privacy and auditability matter.
Experience contributing to a
modular / clean architecture
codebase across multiple bounded contexts.
Prior experience in fast-growing startups where you owned infrastructure other engineers depended on daily.

📌 Senior Ai Platform Engineer Hexcore & Eval Systems (México)
🏢 Nir-yu
📍 México