The Role

We 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 idóneo 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 ideal candidate should be proactive, pragmatic, and able to balance hands-on implementation with strategic technical decision-making.

Key Responsibilities

- Solution 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 systems
- Hands-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 standards
- 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




- Integrations: 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
- Optimization: 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
- Cross 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 engineering
- Team 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 team

Soft Skills

- Business 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 Qualifications

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:

Tech Stack

Generative AI & LLMs

- LLM platforms: Azure OpenAI, OpenAI, Anthropic, AWS Bedrock, Google Vertex AI
- Prompt engineering, structured outputs, function / tool calling
- Model selection, routing, and fine-tuning funda

📌 Solutions Architect – Agentic Ai (Xico)
🏢 Capmation
📍 Xico