Brillio 2

AI Engineer - R01565292

AI Engineering

Guadalajara, Jalisco, MexicohybridEmployee

AI/ML Engineer

Primary Skills

Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio

Specialization

Data Science Advanced: Data Specialist

Job requirements

AI Engineer – Agentic AI Platforms & Applications

About the Role

We are looking for highly motivated AI Engineers to design, build, and deploy next-generation AI agents and autonomous workflows that solve real business problems. You will work closely with product, operations, and business teams to create production-grade agentic applications powered by LLMs, enterprise data, and modern AI orchestration frameworks. This role is ideal for engineers who enjoy rapid experimentation, solving ambiguous problems, and turning AI prototypes into scalable enterprise solutions.

What You’ll Do

Design, build, and deploy AI agents and multi-agent systems using modern LLM frameworks and enterprise AI platforms

Develop agentic workflows for business functions such as Finance, Legal, Operations, Sales, Support, and Growth

Build production-ready applications using LLMs, RAG pipelines, tool calling, memory systems, and orchestration frameworks

Integrate AI agents with enterprise platforms such as Google Workspace, Slack, CRM systems, internal APIs, databases, and knowledge repositories

Evaluate and leverage foundation models across providers (Gemini, OpenAI, Anthropic, open-source models, etc.) based on use case requirements

Work closely with business stakeholders to identify opportunities, prototype solutions rapidly, and iterate based on user feedback

Create reusable agent frameworks, prompt libraries, evaluation pipelines, and deployment patterns

Implement observability, guardrails, evaluation, and monitoring for AI applications in production

Optimize agent performance for latency, accuracy, reliability, and cost

Contribute to internal best practices around agent architecture, prompting, RAG, and AI engineering standards

Stay current with emerging trends in autonomous agents, AI infrastructure, and enterprise AI adoption What We’re Looking For

Strong software engineering fundamentals with experience building scalable backend or full-stack applications

Hands-on experience with LLMs and modern AI application development

Experience building AI agents, autonomous workflows, or agentic applications

Familiarity with frameworks such as LangChain, LangGraph, CrewAI, Google ADK, AutoGen, Semantic Kernel, or similar

Strong understanding of: o RAG architectures o Prompt engineering o Vector databases o Tool/function calling o AI workflow orchestration o Context and memory management

Experience working with cloud platforms such as Google Cloud, AWS, or Azure

Experience with Vertex AI, Gemini Enterprise, OpenAI APIs, or similar enterprise AI platforms is a strong plus

Familiarity with APIs, microservices, event-driven systems, and enterprise integrations

Comfortable working in ambiguous environments with evolving requirements and rapid experimentation cycles

Strong communication skills and ability to collaborate with both technical and non-technical stakeholders

Builder mindset with strong ownership and execution capabilities

Preferred Qualifications

Experience deploying AI applications into production environments

Familiarity with AI evaluation frameworks, observability, and guardrails

Experience with Google Workspace APIs, Slack integrations, or enterprise automation tools

Knowledge of fine-tuning, model optimization, or open-source LLM deployment

Exposure to multi-agent coordination and autonomous decision-making systems

Experience working in fast-paced startup or innovation environments

Experience

4–8 years of software engineering experience

2+ years of hands-on experience building AI/LLM-powered applications preferred Nice to Have

Experience with Python-based AI ecosystems

Knowledge of vector databases such as Pinecone, Weaviate, Chroma, or Vertex AI Vector Search

Experience with Kubernetes, Docker, CI/CD, and cloud-native deployments

Contributions to open-source AI projects or experimentation with emerging agentic frameworks

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