Gerente Asociado Arquitectura de AI

CWP Panama's AI Transformation & Advanced Analytics area is accelerating the adoption of intelligent automation across all business functions (commercial, network, customer experience, finance, operations). The AI Automation Architect is a senior individual contributor responsible for designing end-to-end AI-powered automation architectures that leverage AWS (Bedrock, SageMaker, Step Functions, Lambda, EventBridge) to transform complex business processes into scalable, governed, and measurable intelligent workflows.This role bridges the gap between business objectives and technical execution, defining the technology stack, data pipelines, agentic AI frameworks, and deployment guardrails for enterprise-grade automation in a Telecom environment. The architect will map automation opportunities to eTOM process areas (Fulfillment, Assurance, Billing) and existing BSS/OSS systems to maximize ROI and minimize integration risk.The ideal candidate combines deep technical expertise in cloud-native AI systems with strategic thinking, strong stakeholder management, and the ability to articulate complex architectures to both engineering teams and C-Level executives. Architecture & Intelligent WorkflowsDesign scalable AI automation architectures on AWS, leveraging Bedrock Agents, Step Functions, Lambda, and EventBridge for workflow orchestrationDefine agentic AI frameworks that combine Foundation Models with tool-use, memory, and multi-step reasoning capabilitiesCreate reference architectures and reusable patterns for common Telco automation scenarios (e.g., network fault resolution, customer intent routing, billing disputes)Establish architecture standards, documentation practices, and technical debt management for AI systemsModel & Stack Selection (AWS/Bedrock-Centric)Evaluate and select Foundation Models via Amazon Bedrock (Claude, Titan, Llama, Mistral) based on cost, latency, and task-fit analysisDefine integration patterns: RAG (Bedrock Knowledge Bases), fine-tuning (SageMaker), prompt engineering, and hybrid approachesDesign vector storage strategies using Amazon OpenSearch Serverless or Aurora pgvector for enterprise knowledge retrievalConduct build-vs-buy analysis for automation components, favoring AWS-native services over third-party when comparableSystem Integration (Telco BSS/OSS)Align AI automation architectures with existing Telco infrastructure: BSS (billing, CRM, order management) and OSS (network management, fault correlation, service assurance)Map automation opportunities to eTOM Level 2/3 processes to ensure organizational alignment and prioritizationDesign API layers and event-driven integrations between AI systems and legacy platformsCollaborate with IT and Network teams to ensure production-grade connectivity, latency requirements, and failover strategiesGovernance, Security & FinOpsEstablish AI governance frameworks: model performance monitoring (drift detection), bias detection, explainability, and human-in-the-loop controlsEnsure compliance with Panama data protection regulations (Ley 81 de Proteccion de Datos Personales) and ASEP Telecom regulatory requirementsDefine PII handling protocols, data classification, and access control policies for AI pipelinesImplement FinOps practices for AI workloads: cost allocation, token usage monitoring, model cost-per-inference optimizationDesign security guardrails for LLM deployments (prompt injection mitigation, output filtering, audit trails)Stakeholder Management & DeliveryCollaborate with CDO, CTO, VP Network, VP Commercial, and Security teams to prioritize automation opportunities using Size-of-the-Prize vs. Complexity frameworksTranslate technical architectures into executive-friendly narratives for business case approval and budget allocationDefine success metrics (automation rate, cycle time reduction, cost-per-transaction, NPS impact) and measurement frameworksLead technical design reviews, architecture decision records (ADRs), and proof-of-concept validationsMentor data engineers and ML engineers on best practices for production AI system design Education / QualificationsBachelor's degree in Computer Science, Data Science, Software Engineering, or related technical field (required)Fully proficient in English and Spanish (written and verbal) - both languages used dailyAWS certifications strongly preferred: Solutions Architect Professional, Machine Learning Specialty, or AI PractitionerExperience5+ years of experience in software architecture, ML engineering, or AI solutions design1+ years hands-on with LLM-based systems (RAG, agents, prompt engineering, fine-tuning)Demonstrated experience designing and deploying production AI/ML systems on AWS (Bedrock, SageMaker, Lambda)Experience with event-driven architectures and workflow orchestration (Step Functions, EventBridge, or equivalent)Track record of translating business requirements into scalable technical architectures with measurable ROIExperience working with cross-functional teams (engineering, product, business, security) in complex organizationsTechnical Skills & AbilitiesLLM & AI Orchestration: Proficiency with Amazon Bedrock (Agents, Knowledge Bases, Guardrails), and multi-model orchestration patternsCloud Architecture: Deep expertise in AWS services (Lambda, Step Functions, EventBridge, API Gateway, SQS/SNS, DynamoDB, S3)Data Pipelines: Experience with vector databases (OpenSearch Serverless, pgvector), data lakes, and analytics platforms (Redshift, Athena, QuickSight)Infrastructure as Code: Terraform, CloudFormation, or CDK for reproducible AI infrastructure deploymentsProgramming: Python (primary), with working knowledge of Node.js/TypeScript for serverless functionsStrong executive communication skills - ability to present architecture decisions and trade-offs to C-Level stakeholdersComplex change management and ability to drive AI adoption in organizations with legacy processesStrong project management skills in Agile environments (Scrum/Kanban)