AI/ML EngineerAI Engineer - Agentic AI Platforms & ApplicationsCore Technical SkillsHypothesis TestingT-TestZ-TestRegression (Linear, Logistic)Python/PySparkSAS/SPSSStatistical analysis and computingProbabilistic Graph ModelsGreat ExpectationEvidently AIForecasting (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 StudioData Science Advanced: Data SpecialistAbout the RoleWe 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 adecuado for engineers who enjoy rapid experimentation, solving ambiguous problems, and turning AI prototypes into scalable enterprise solutions.What You'll DoDesign, build, and deploy AI agents and multi-agent systems using modern LLM frameworks and enterprise AI platformsDevelop agentic workflows for business functions such as Finance, Legal, Operations, Sales, Support, and GrowthBuild production-ready applications using LLMs, RAG pipelines, tool calling, memory systems, and orchestration frameworksIntegrate AI agents with enterprise platforms such as Google Workspace, Slack, CRM systems, internal APIs, databases,



and knowledge repositoriesEvaluate and leverage foundation models across providers (Gemini, OpenAI, Anthropic, open-source models, etc.) based on use case requirementsWork closely with business stakeholders to identify opportunities, prototype solutions rapidly, and iterate based on user feedbackCreate reusable agent frameworks, prompt libraries, evaluation pipelines, and deployment patternsImplement observability, guardrails, evaluation, and monitoring for AI applications in productionOptimize agent performance for latency, accuracy, reliability, and costContribute to internal best practices around agent architecture, prompting, RAG, and AI engineering standardsStay current with emerging trends in autonomous agents, AI infrastructure, and enterprise AI adoptionWhat We're Looking ForStrong software engineering fundamentals with experience building scalable backend or full-stack applicationsHands-on experience with LLMs and modern AI application developmentExperience building AI agents, autonomous workflows, or agentic applicationsFamiliarity with frameworks such as LangChain, LangGraph, CrewAI, Google ADK, AutoGen, Semantic Kernel, or similarStrong understanding of: RAG architectures, Prompt engineering, Vector databases, Tool/function calling,



AI workflow orchestration, Context and memory managementExperience working with cloud platforms such as Google Cloud, AWS, or AzureExperience with Vertex AI, Gemini Enterprise, OpenAI APIs, or similar enterprise AI platforms is a strong plusFamiliarity with APIs, microservices, event-driven systems, and enterprise integrationsComfortable working in ambiguous environments with evolving requirements and rapid experimentation cyclesStrong communication skills and ability to collaborate with both technical and non-technical stakeholdersBuilder mindset with strong ownership and execution capabilitiesPreferred QualificationsExperience deploying AI applications into production environmentsFamiliarity with AI evaluation frameworks, observability, and guardrailsExperience with Google Workspace APIs, Slack integrations, or enterprise automation toolsKnowledge of fine-tuning, model optimization, or open-source LLM deploymentExposure to multi-agent coordination and autonomous decision-making systemsExperience working in fast-paced startup or innovation environmentsExperience4-8 years of software engineering experience2+ years of hands-on experience building AI/LLM-powered applications preferredNice to HaveExperience with Python-based AI ecosystemsKnowledge of vector databases such as Pinecone, Weaviate, Chroma, or Vertex AI Vector SearchExperience with Kubernetes, Docker, CI/CD, and cloud-native deploymentsContributions to open-source AI projects or experimentation with emerging agentic frameworks#J-*****-Ljbffr

📌 Ai Engineer292 (Jalisco)
🏢 Brillio 2
📍 Jalisco