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)
🏢 Slalom
📍 Monterrey