What You'll DoProvide thought leadership on AI/ML, Generative AI, and Agentic AI internally and with clients, while contributing to a culture of collaboration, learning, and curiosityDesign end-to-end agentic AI architectures including planning loops, memory management, tool integration, and agent coordination patternsArchitect multi-agent orchestration systems using frameworks such asStrands Agents SDK,OpenAI Agents SDK, Google ADK, LangGraphor similar for autonomous reasoning, decision-making, and task executionDesign and implement Model Context Protocol (MCP) server integrations for tool use, data access, and cross-system interoperability with enterprise systemsBuild advanced retrieval-augmented generation (RAG) systems including vector databases, embedding strategies, chunking optimization, hybrid search, re-ranking, and multi-source data synthesisDesign and deliver AI and ML solutions across AWS, Azure, and GCP, using the right mix combination of cloud-native data services, ML tooling, LLM platforms, and software engineering practicesBuild in Python and, where useful, other languages to deliver machine learning systems, APIs, evaluation harnesses, retrieval pipelines, agent workflows, and production servicesRecommend and implement architecture for model and agent pipelines, CI/CD, testing, deployment, observability, andMLOps/LLMOpsat scaleImplement evaluation frameworks (e.G., RAGAS, DeepEval, LangSmith) to measure task success rates, tool-call accuracy, and reasoning integrity for GenAI systemsBuild guardrails for safety, compliance, and performance monitoring including human-in-the-loop (HITL) approval workflows, escalation policies, and sandbox isolationDefine AI governance frameworks including model risk management, responsible AI practices, regulatory compliance,



and authorization boundaries for autonomous decision-makingExplain model and system behavior to both technical and non-technical audiences, including leading deep technical presentations, workshops, and architecture conversationsCollaborate with Product Owners to apply Slalom's agile process and lead the initiation, delivery, and transition of projects in a client-facing roleLead and mentor engineers and machine learning practitioners.
Lead smaller projects (3 to 5 people) as the technical lead from project initiation to deliveryBuild trusted relationships with customers and collaborate across Slalom teams to share learnings and strengthen the broader Intelligence Engineering practiceWill be delivery-focused approximately *****% of the timeWillingness to travel up to 50%, at peak timesWe are looking for candidates who are interested in working in a hybrid environment as we build the foundation and grow our team in Mexico.
We offer a adaptable working environment to balance the need to work independently, with days that may require in-person collaboration at our office.What You'll Bring5+ years of software engineering experience building and deploying production systems; experience with machinelearning, applied AI, or intelligent software systems is a plus, with 2+ years focused on generative AI, LLMs, or agentic AI systemsHands‐on experience designing or building multi‐agent systems including agent orchestration, tool integration, and autonomous decision‐making workflowsProficiency with at least one agentic AI or workflow framework such asLangGraph,Strands,AutoGen,CrewAI, Semantic Kernel, OpenAI Agents SDK,



Google ADK, or similarExperience with RAG architectures including vector databases, embeddings, and retrieval optimization,and context management techniques such as chunking, summarization, and memory handlingExperience developing production‐ready solutions on at least one major cloud AI platform, such as AWS Bedrock, Azure AI Foundry/OpenAI Service, GCP Vertex AI/Gemini, or Databricks; experience operating and maintaining production environments is a plusExperience with AI‐assisted development tools such as Claude Code, Cursor, Kiro, or similar IDE‐based coding agents, including effective use for code generation, refactoring, debugging, and developer workflow accelerationStrong Python development skills; experience withFastAPI,Flask, or equivalent API frameworksExperience building ML or AI systems end to end, including data access, feature or retrieval flows, APIs, testing, deployment, and production supportFamiliarity with evaluation frameworks, tracing, observability, model behavior analysis, and regression testing for GenAI systemsUnderstanding of prompt engineering, LLM fine‐tuning, chain‐of‐thought reasoning, and structured output techniquesRecognized as an authority on at least one technical domain (e.G., Agentic Systems, RAG, Multi‐Agent Orchestration) with generalist familiarity across AI/ML techniquesAbility to work across new domains and unfamiliar data structures and lead exploratory analysis when requirements are not fully definedExcellent verbal and written communication skills; ability to lead highly technical presentationsFamiliarity with Agile project delivery(Preferred) Experience with Model Context Protocol (MCP) server development and integration(Preferred) Experience with MLOps/LLMOps pipelines, CI/CD for ML, and model monitoring/observability#J-*****-Ljbffr

📌 Agentic Ai Architect - Intelligence Engineering (Monterrey)
🏢 Slalom
📍 Monterrey