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 asStrands Agents SDK,OpenAI Agents SDK, Google ADK, LangGraphor 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, andMLOps/LLMOpsat scale

Implement evaluation frameworks (e.g., RAGAS, DeepEval, LangSmith) to measure task success rates, tool-call accuracy, and reasoning integrity for GenAI 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 versátil 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 asLangGraph,Strands,AutoGen,CrewAI, Semantic Kernel, OpenAI 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/OpenAI 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 withFastAPI, 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 GenAI 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

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📌 Agentic AI Architect - Intelligence Engineering (Monterrey) (Ejido de la Finca)
🏢 Slalom
📍 Ejido de la Finca