30+ days ago Requisition ID: ****
Salary Range: $72,****** To $84,****** Annually
Established in ****, Softeq was built from the ground up to specialize in new product development and R&D;, tackling the most difficult problems in the tech sphere. Now we've expanded to offer early-stage innovation and ideation plus digital transformation business consulting. Our superpower is to deliver all of this under one roof on a integral scale. So let's get started and build a better future together!
We're looking for a
Senior AI Engineer
to join our team and contribute to its continued growth.
We are building a new customer portal for a large logistics company. Business users at the company's customers will use it to track shipments, see delays and ETAs, manage invoices and documents, and get reports.
An AI assistant answers customers' questions about their own operational and financial data. A working prototype exists; our job is to take it to production for external customers.
You will own the AI part of the product: make the assistant safe, accurate and measurable, and build the next AI features on top of it.
What you will do
Take the existing AI assistant from prototype to production: retrieval with Azure AI Search, tool calls over entitlement-scoped APIs, prompt and model versioning.
Design narrow, typed tools for the assistant so that every answer respects the customer's data entitlements. The customer is identified from the authenticated session, never from the model's output.
Ground answers in certified metrics from the lakehouse semantic layer, and show users where an answer comes from.
Build evaluation pipelines: golden sets, groundedness and correctness metrics, tool-call accuracy, regression runs in CI that block a bad release.
Run the AI gateway on Azure API Management: quotas, logging, content safety, model routing and cost control.




Implement guardrails: prompt-injection defense, PII redaction, safe handling of retrieved content, human confirmation for sensitive actions.
Build new AI features: daily briefings, AI-assisted support and knowledge search, personalized recommendations.
Work with the Data Scientist to put predictive models (delays, congestion, cost) into the product: model serving, monitoring and drift alerts.
Monitor the assistant in production: tracing, user feedback, quality and cost dashboards.
What we expect
3+ years building
LLM applications in production for external users
: RAG, function / tool calling, evaluation. Internal tools or pet projects alone are not enough.
Strong
Python
in production: FastAPI or similar, async, tests.
Hands-on experience with
evaluation of LLM systems
: you can show how you built a golden set, which metrics you tracked, and how results gated releases.
Practical
AI security
: prompt injection (direct and indirect), data leakage between customers, least-privilege tools.
Experience with
multi-tenant data isolation
: you know why a customer filter must live in the server or the data layer, not in the prompt.
Good
SQL
and understanding of business metrics: you can write the query behind "on-time delivery % per customer last month" and know where the metric definition should live.
Clear written and spoken English; comfortable explaining design trade-offs to client architects and security teams.
Nice to have
Databricks: Mosaic AI, Model Serving, MLflow, Unity Catalog, metric views.
MCP servers in production, with authentication and per-tool authorization.
Agent frameworks (LangGraph, Semantic Kernel or similar) and an opinion on when not to use them.
Observability for LLM apps (Langfuse, OpenTelemetry, App Insights).
Logistics, transportation or supply-chain domain.
#J-*****-Ljbffr

📌 Senior I Ai Engineer (Jalisco)
🏢 Softeq
📍 Jalisco