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Remote in Argentina, & 4 others
AI Solution Engineering& 9 others
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We are looking for an AI Solution Architect to join our team. This role is a great fit for someone who enjoys guiding teams through the practical realities of building and validating AI-driven systems. You'll work closely with both technical and business stakeholders, helping bridge the gap between emerging AI capabilities and real-world application. If you're motivated by hands-on experimentation paired with clear, structured advisory work, this position offers a strong opportunity to contribute meaningfully
Responsibilities
- Provide practical guidance on agent development, including model and tool selection, deployment approaches, evaluation methods, and ongoing monitoring
- Evaluate RAG/GraphRAG approaches, chunking strategies, retrieval methods, re-ranking techniques, and vector-store options alongside the architect, implementing only the approach confirmed in the agreed validation plan
- Define golden datasets, quality thresholds, and regression tests using approved, controlled test data that excludes personal information
- Build the agreed nonproduction agent workflow in Python when readiness and capacity allow, including its associated tools and a single API/system integration
- Test success paths, error handling, and permission scenarios, documenting quality outcomes, usage costs, limitations, and any remaining gaps
- Draft a workflow deployment and reuse blueprint along with engineering playbooks, covering semantic/context handling, identity management, permissions, and autonomy controls
- Explain AI-assisted development approaches and agent tooling decisions clearly to business users, engineering teams, and the AI team
- Document practical examples and lead walkthroughs, with advisory work covering production-grade lifecycle guidance, while actual production deployment and ongoing operations fall outside this scope
Requirements
- A minimum of 5 years of relevant experience
- At least 2 years in Senior, Lead, Manager, or Owner-level roles
- Proven track record managing engagements for teams of 5 or more full-time employees
- Hands-on experience in AI agent development, including design, deployment, and iteration
- Working knowledge of Amazon Web Services for supporting cloud-based AI solutions
- Experience in GenAI agent evaluation and testing to ensure quality, reliability, and performance
- Strong proficiency in Python for building and testing AI-driven workflows
- Solid understanding of REST APIs for enabling system and tool integrations
- Practical experience with Retrieval-Augmented Generation (RAG) for grounding AI outputs in relevant data
- Professional proficiency in English (C1)
Nice to have
- Experience with AWS Bedrock for building and deploying generative AI solutions
- Familiarity with Amazon Bedrock AgentCore for supporting agent orchestration and management
- Background working with MuleSoft for enabling system integrations
- Relevant certification, which would be considered a valuable addition to your qualifications