We are seeking a Solutions Architect to join our Engineering Team. The adecuado candidate is a senior technical leader who can define end-to-end AI solution architecture, guide engineering standards, solve complex integration, retrieval, and orchestration problems, and support the delivery of secure, reliable, and cost-efficient AI applications in a fast-moving environment.
The Solutions Architect must work effectively with engineers, product stakeholders, clients, and delivery teams, while providing technical guidance, mentoring engineering teams, and driving high engineering quality. Lead the architecture of agentic AI solutions end-to-end, combining LLMs, RAG pipelines, AI agents, APIs, and vector databases into scalable, secure, and maintainable systems
Agent & RAG Design: Design multi-agent orchestration, tool/function calling, memory, and retrieval strategies (chunking, embeddings, hybrid search, reranking) with guardrails and human-in-the-loop controls built in by default
Lead the integration of AI components with enterprise APIs, data platforms, and SaaS/line-of-business systems, ensuring robust error handling, idempotency, security, and observability
Testing & Evaluation: Define and enforce evaluation frameworks for LLM and agent outputs (accuracy, groundedness, hallucination rate, latency, and cost), including regression testing and automated quality gates
Drive performance and cost optimization through prompt engineering, model selection and routing, caching, token management, and retrieval tuning
Operational Excellence: Own production health of AI workloads through tracing, monitoring, and LLMOps practices; lead incident triage and root-cause analysis for complex issues
Responsible AI & Governance:



Ensure solutions meet data privacy, PII handling, security, and responsible-AI requirements, aligned with company AI governance policies
Provide technical guidance and mentor engineers at all levels while also leading training sessions, design and code reviews, providing constructive feedback, and aligning technical standards across the team
Accountability: Accountable for the technical and delivery success of AI solutions or projects, taking ownership of outcomes across teams and addressing issues proactively rather than reactively.
Judgement: Demonstrate judgment by making high-impact decisions, balancing innovation and short-term delivery with long-term sustainability, security, and escalating risks early.
Experience: Over 6+ years of software engineering experience, including 3+ years in an architect or technical lead role and 2+ years building LLM-based applications, in the following:
Agent memory, planning, and human-in-the-loop workflows
RAG & Vector Databases
Embedding models, chunking strategies, hybrid search, and reranking
Document ingestion and data preparation pipelines
RESTful and GraphQL API design, JWT / OAuth2 authentication
FastAPI, ASP.NET Core, or equivalent API frameworks
Azure, AWS, or GCP cloud-native services
Docker, Kubernetes




Secrets and identity management (Key Vault, Managed Identities)
Caching and cost-control patterns for AI workloads
DevOps & LLMOps
Terraform or Bicep for Infrastructure as Code
Testing & Evaluation
Unit, integration, and API testing (pytest, Postman / Bruno)
Guardrails and safety testing (content filtering, prompt-injection defense)
Application Insights, Log Analytics, or equivalent monitoring platforms
Hands-on experience with AI frameworks such as LangChain / LangGraph, Semantic Kernel, or LlamaIndex, and with at least one major LLM platform (Azure OpenAI, OpenAI, Anthropic, AWS Bedrock).
Strong experience with vector databases and retrieval design, including embeddings, chunking, hybrid search, and reranking.
Deep proficiency in Python and solid API design and integration skills, building and securing RESTful services that connect AI components with enterprise systems.
Track record leading solution architecture for enterprise-scale systems, including non-functional requirements, trade-off analysis, and architecture governance.
Experience defining evaluation, testing, and optimization strategies for AI applications (quality, latency, and cost).
Cloud-native deployment experience on Azure, AWS, or GCP, including containers, CI/CD pipelines, and Infrastructure as Code.
Familiarity with AI governance and risk frameworks (NIST AI RMF, ISO/IEC 42001) and data privacy regulations.
Experience with C# / .NET and Semantic Kernel in Microsoft-centric environments.
Cloud or AI certifications (e.g., Azure Solutions Architect Expert, Azure AI Engineer Associate, AWS Solutions Architect Professional).
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📌 Senior Solution Architect AWS (Ciudad de México)
🏢 Nodi
📍 Ciudad de México