The RoleWe are seeking a Solutions Architect to join our Engineering Team.
This role combines deep hands-on engineering capability in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents with technical leadership in designing, developing, and deploying scalable agentic AI solutions for enterprise environments.The ideal 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.This position also requires strong collaboration and leadership skills.
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.
The adecuado candidate should be proactive, pragmatic, and able to balance hands-on implementation with strategic technical decision-making.Key ResponsibilitiesSolution Architecture: 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 systemsHands-on Engineering: Design, develop, and deploy AI applications and agent workflows, while leading evaluation and adoption of new AI frameworks, models, and methodologies to improve company standardsAgent & 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 defaultIntegrations: Lead the integration of AI components with enterprise APIs, data platforms, and SaaS/line-of-business systems, ensuring robust error handling, idempotency, security, and observabilityTesting & Evaluation: Define and enforce evaluation frameworks for LLM and agent outputs (accuracy, groundedness, hallucination rate, latency, and cost), including regression testing and automated quality gatesOptimization: Drive performance and cost optimization through prompt engineering, model selection and routing, caching, token management, and retrieval tuningOperational Excellence: Own production health of AI workloads through tracing, monitoring, and LLMOps practices; lead incident triage and root-cause analysis for complex issuesResponsible AI & Governance: Ensure solutions meet data privacy, PII handling, security, and responsible-AI requirements, aligned with company AI governance policiesCross Functional Collaboration: Partner with business ops, stakeholders, and clients to translate business requirements into AI solution designs and act as the intermediary between business operations and engineeringTeam Development:



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 teamSoft SkillsBusiness Acumen: Connect AI architecture decisions to business outcomes, anticipating impacts on cost, risk, and value, and providing decisions to maximize long-term value.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.Communication: Communicate complex AI and architecture concepts clearly to both technical and non-technical audiences, aligning stakeholders, and enabling confident decision making.Judgement: Demonstrate judgment by making high-impact decisions, balancing innovation and short-term delivery with long-term sustainability, security, and escalating risks early.Collaboration: Drive alignment across multiple teams and disciplines by acting as a unifying technical leader, resolving cross-team friction.Curiosity: Maintain curiosity about the rapidly evolving AI landscape and emerging technologies, using that understanding to anticipate challenges, guide innovation, and continuously improve technical and delivery practices.Required QualificationsExperience: 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:Tech StackGenerative AI & LLMsLLM platforms: Azure OpenAI, OpenAI, Anthropic, AWS Bedrock, Google Vertex AIPrompt engineering, structured outputs, function / tool callingModel selection, routing, and fine-tuning fundamentalsAgentic AI & FrameworksLangChain / LangGraph, Semantic Kernel, LlamaIndexMulti-agent frameworks: CrewAI, AutoGen, or equivalentsModel Context Protocol (MCP) and agent-to-agent communication patternsAgent memory, planning, and human-in-the-loop workflowsRAG & Vector DatabasesVector databases: Pinecone, Weaviate, Qdrant, pgvector, Azure AI SearchEmbedding models, chunking strategies, hybrid search, and rerankingDocument ingestion and data preparation pipelinesLanguages & APIsPython (primary); C# / .
NET or TypeScripta plusRESTful and GraphQL API design, JWT / OAuth2 authenticationFastAPI, ASP.NET Core, or equivalent API frameworksCloud & InfrastructureAzure, AWS, or GCP cloud-native servicesContainers and orchestration:



Docker, KubernetesServerless compute (Azure Functions, AWS Lambda)Secrets and identity management (Key Vault, Managed Identities)Architecture & PatternsAgentic and RAG reference architecturesEvent-driven, domain-oriented microservicesResiliency patterns (retries, circuit breakers, fallbacks) for LLM callsCaching and cost-control patterns for AI workloadsDevOps & LLMOpsCI/CD pipelines (Azure DevOps, GitHub Actions)Terraform or Bicep for Infrastructure as CodePrompt and model versioning, Git branching and pull request workflowsTesting & EvaluationLLM evaluation frameworks: Ragas, DeepEval, promptfoo, or equivalentsUnit, integration, and API testing (pytest, Postman / Bruno)Guardrails and safety testing (content filtering, prompt-injection defense)Observability & OperationsLLM tracing: LangSmith, Langfuse, OpenTelemetryApplication Insights, Log Analytics, or equivalent monitoring platformsMust have:Proven experience designing, developing, and deploying LLM-based applications to production, including RAG pipelines and agentic / multi-agent workflows.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.Demonstrated ability to provide technical guidance and mentor engineering teams.Preferred QualificationsExperience with the Model Context Protocol (MCP) and building tool ecosystems for AI agents.Experience with LLMOps practices: prompt versioning, evaluation pipelines, and LLM tracing (LangSmith, Langfuse, OpenTelemetry).
Background in Domain-Driven Design and event-driven architecture.Familiarity with AI governance and risk frameworks (NIST AI RMF, ISO/IEC *****) and data privacy regulations.Experience with C# / .
NET and Semantic Kernel in Microsoft-centric environments.Consulting or client-facing delivery experience, including discovery and whiteboard sessions.Cloud or AI certifications (e.G., Azure Solutions Architect Expert, Azure AI Engineer Associate, AWS Solutions Architect Professional).
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📌 Solutions Architect – Agentic Ai (Xico)
🏢 Capmation
📍 Xico