• Design, develop, and deploy production-grade applications leveraging various LLMs, context optimization, etc. • Architect and implement sophisticated, multi-step and multi-agent workflows using frameworks like LangChain and LangGraph. • Build and optimize RAG pipelines, including implementing and managing embeddings, vector databases, and advanced rerankers to enhance response quality and relevance. • Use code generation applications (e.g. Replit, Cursor, Google AI Studio, Git Hub Copilot in Agent mode, etc.) to create full applications (including frontend and backend), generate tests, perform testing and integrate them in the core product without writing any code. • Lead efforts in LLM fine-tuning (e.g., LoRA, QLoRA) for specific domain knowledge and tasks, and implement strategies for efficiency. • Develop and refine advanced prompt engineering techniques to maximize model performance, consistency, and safety. • Own end-to-end implementation from frontend to the backend. • Expose AI/LLM functionality written in Python using Java services, leverage multi-threading capabilities in Java to augment AI/LLM functionality developed in Python • Utilize AI-powered development tools (e.g., GitHub Copilot, etc.) to efficiently generate, refactor, and optimize high-quality code. • Work closely with team leads managers, QA, product managers and team in the US (this will require the willingness to partially work US working hours)

• Diseñar y construir con alto apalancamiento de IA, un núcleo de software simple, robusto y bien probado. • Construir de punta a punta (full-stack), diseñas e implementas funcionalidades completas del ciclo de renta. • Usar agentes de IA como forma principal de construir. • Revisar y validar críticamente el código propio, del equipo o generado por IA. • Proponer estándares, documentación y mejores prácticas.

Role Description Our client, a leading technology organization, is seeking a Senior AI Engineer to design and deliver scalable Generative AI solutions in a production environment. The role focuses on building enterprise-grade AI products using Python, React, and AWS while leveraging modern architectures including LLM pipelines, RAG, agentic systems, and MCP frameworks. The ideal candidate brings deep Gen AI expertise, strong cloud engineering fundamentals, and the ability to lead cross-functional delivery in an Agile environment. Key Responsibilities - Design, architect, and build scalable Gen AI solutions including LLM pipelines, Agentic AI, MCP, Graph/RAG architectures, and prompt-based applications - Lead end-to-end delivery of production-ready AI products using Python, React/Next.js, and AWS - Implement cloud-native solutions across AWS EKS, Lambda, Fargate, Glue, and Athena - Leverage Gen AI frameworks including LangChain, LlamaIndex, and Amazon Bedrock - Design and optimize vector and graph database solutions — Weaviate, Milvus, PGVector, Neo4J, Neptune - Build and orchestrate ETL pipelines using Apache Airflow/DAGs - Optimize AI product performance and drive continuous experimentation with cutting-edge Gen AI methods and toolchains - Serve as SME on Gen AI technologies and actively contribute to shaping the organization's AI roadmap - Own timelines, deliverables, and project milestones using Agile Scrum/Kanban practices - Monitor operational metrics and incident data to drive continuous improvement and reliability - Ensure adherence to DevSecOps protocols, governance standards, and SDLC best practices Qualifications - 7+ years of progressive engineering experience with 1–2 years leading AI or emerging tech initiatives - Hands-on expertise with Gen AI models — GPT, Claude, Gemini, LLaMA — and advanced prompt engineering - Strong experience with Agentic AI, MCP, and Graph/RAG architectures - Proficiency in Gen AI frameworks — LangChain, LlamaIndex, Amazon Bedrock - Web development proficiency in Next.js, React, and TypeScript/JavaScript - Deep AWS expertise — EC2, EKS, Fargate, Lambda, Athena, Glue, Lake Formation - Infrastructure as Code experience — Terraform, Docker, Puppet - Strong Python programming skills — NumPy, Pandas, Matplotlib, Boto3 - Vector and graph database experience — Weaviate, Milvus, PGVector, Neo4J, Neptune - ETL orchestration experience using Apache Airflow/DAGs - Experience with automated testing frameworks — Ragas, Playwright, Selenium, Zephyr - Familiarity with LLM fine-tuning techniques - Strong understanding of SDLC best practices, DevSecOps, and Agile Scrum/Kanban - Experience with JIRA, Confluence, and JIRA Align Nice-to-Have Skills - Experience with BI tools such as QuickSight or Tableau - Knowledge of financial markets and enterprise data systems - Background working in highly regulated or enterprise-scale environments

Role Description We are building a next-generation AI Payments Engineering Team to design and deliver an AI-native Payments Operating System. This team will combine AI engineering, payments domain expertise, and production automation to transform high-effort, high-risk financial operations into scalable, intelligent, and auditable systems. The team will focus on automating critical Payments processes such as: - Least cost routing - Reconciliations - Audit - Incident automation The mission is to deliver efficiency with control integrity—not just automation speed. Qualifications - AIOps Engineer - AI Automation Engineer - Senior AI Engineer (Payments & Architecture) Requirements - Design and deploy production-grade AI automation workflows that drive immediate operational impact. - Build and deploy agentic workflows for: - Reconciliations - Audit & reporting preparation Benefits - Experience-led everything - Commitment to creating an inclusive workplace - Equal employment opportunity (EEO) and affirmative action employer