Lead AI/ML Engineer - R01565293

Mexico·GuadalajaraEmployeelead

Machine Learning EngineerData

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Overview

AI/ML Engineer Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation,

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Machine Learning EngineerData

AI/ML Engineer

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

Data Science Advanced: Data Specialist

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