What You’ll Do Provide thought leadership on AI/ML, Generative AI, and Agentic AI internally and with clients, while contributing to a culture of collaboration, learning, and curiosity Design end-to-end agentic AI architectures including planning loops, memory management, tool integration, and agent coordination patterns Architect multi-agent orchestration systems using frameworks such as Strands Agents SDK, Open AI Agents SDK, Google ADK, Lang Graphor similar for autonomous reasoning, decision-making, and task execution Design and implement Model Context Protocol (MCP) server integrations for tool use, data access, and cross-system interoperability with enterprise systems Build advanced retrieval-augmented generation (RAG) systems including vector databases, embedding strategies, chunking optimization, hybrid search, re-ranking, and multi-source data synthesis Design 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 practices Build in Python and, where useful, other languages to deliver machine learning systems, APIs, evaluation harnesses, retrieval pipelines, agent workflows, and production services Recommend and implement architecture for model and agent pipelines, CI/CD, testing, deployment, observability, and MLOps/LLMOpsat scale Implement evaluation frameworks (e.g., RAGAS, Deep Eval, Lang Smith) to measure task success rates, tool-call accuracy, and reasoning integrity for Gen AI systems Build guardrails for safety, compliance, and performance monitoring including human-in-the-loop (HITL) approval workflows, escalation policies, and sandbox isolation Define AI governance frameworks including model risk management, responsible AI practices, regulatory compliance,



and authorization boundaries for autonomous decision-making Explain model and system behavior to both technical and non-technical audiences, including leading deep technical presentations, workshops, and architecture conversations Collaborate with Product Owners to apply Slalom’s agile process and lead the initiation, delivery, and transition of projects in a client-facing role Lead and mentor engineers and machine learning practitioners. Lead smaller projects (3 to 5 people) as the technical lead from project initiation to delivery Build trusted relationships with customers and collaborate across Slalom teams to share learnings and strengthen the broader Intelligence Engineering practice Will be delivery-focused approximately 85–95% of the time Willingness to travel up to 50%, at peak times

We 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 Bring 5+ years of software engineering experience building and deploying production systems; experience with machine learning, applied AI, or intelligent software systems is a plus, with 2+ years focused on generative AI, LLMs, or agentic AI systems Hands‑on experience designing or building multi‑agent systems including agent orchestration, tool integration, and autonomous decision‑making workflows Proficiency with at least one agentic AI or workflow framework such as Lang Graph, Strands, Auto Gen, Crew AI, Semantic Kernel, Open AI Agents SDK,



Google ADK, or similar Experience with RAG architectures including vector databases, embeddings, and retrieval optimization,and context management techniques such as chunking, summarization, and memory handling Experience developing production‑ready solutions on at least one major cloud AI platform, such as AWS Bedrock, Azure AI Foundry/Open AI Service, GCP Vertex AI/Gemini, or Databricks; experience operating and maintaining production environments is a plus Experience 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 acceleration Strong Python development skills; experience with Fast API, Flask, or equivalent API frameworks Experience building ML or AI systems end to end, including data access, feature or retrieval flows, APIs, testing, deployment, and production support Familiarity with evaluation frameworks, tracing, observability, model behavior analysis, and regression testing for Gen AI systems Understanding of prompt engineering, LLM fine‑tuning, chain‑of‑thought reasoning, and structured output techniques Recognized as an authority on at least one technical domain (e.g., Agentic Systems, RAG, Multi‑Agent Orchestration) with generalist familiarity across AI/ML techniques Ability to work across new domains and unfamiliar data structures and lead exploratory analysis when requirements are not fully defined Excellent verbal and written communication skills; ability to lead highly technical presentations Familiarity 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-18808-Ljbffr

📌 Agentic ai architect - intelligence engineering (monterrey)
🏢 Slalom
📍 Monterrey