Senior I AI Engineer

Softeq; Región Centro; Presencial; MXN 1 343 000 - 1 567 000; Tempo integral

Resumo da oferta

Softeq, established in 1997, is expanding its team with a Senior AI Engineer to build and productionize AI-powered customer solutions for a logistics portal, ensuring safety, accuracy and measurable outcomes.

You will own the AI segment, implement guardrails, design tool calls, integrate with Azure services, and collaborate with data scientists to deploy predictive models while maintaining multi-tenant data isolation and robust observability.

Qualificações

• 3+ years building LLM applications in production for external users.
• Strong Python in production environments (FastAPI or similar).
• Hands-on experience with evaluating LLM systems in production.
• Practical AI security: prompt-injection defense & data leakage prevention.
• Experience with multi-tenant data isolation and server-side filtering.
• Good SQL skills and ability to derive business metrics from data.

Responsabilidades

• Move the AI assistant from prototype to production with Azure AI Search and tool calls.
• Design typed tools so answers respect customer entitlements.
• Ground answers in metrics from the data lakehouse and show data provenance.
• Build evaluation pipelines and CI gates to prevent bad releases.
• Run and manage the AI gateway on Azure API Management (quotas, logging, safety).
• Implement guardrails for prompt injections and data handling, with PII redaction when needed.
• Build new AI features such as daily briefings and knowledge search capabilities.
• Collaborate with data scientists to deploy predictive models (delays, congestion, cost).
• Monitor production with tracing, feedback loops, and dashboards.

Conhecimentos

LLM applications in production

Python (production)

SQL

English communication

Ferramentas

Azure AI Search

Azure API Management

Databricks

MLflow

Unity Catalog

Descrição da oferta de emprego

30+ days ago Requisition ID: 1096

Salary Range: $72,000.00 To $84,000.00 Annually

Established in 1997, 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 global 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.