Senior Cloud Solution Architect
Há 2 dias
We are seeking a visionary Cloud Innovation Leader to architect scalable, AI-driven solutions that integrate advanced data processing, intuitive web interfaces, and intelligent automation.
This role demands a strategic leader who thrives in complexity and is deeply committed to cultivating strong stakeholder relationships. As a key member of our team, you will play a pivotal role in fostering a culture of innovation and collaboration, guiding cross-functional teams to deliver solutions that drive pricing transparency, operational efficiency, and market competitiveness.
The ideal candidate will bring a unique blend of technical depth and strategic foresight, with a proven track record of leveraging cutting-edge technologies to deliver end-to-end automation at scale. The role involves interfacing with Enterprise Architecture, Business leaders, Senior Leaders, and Cyber/Privacy/Legal teams.
Key Responsibilities:- Champion Software Excellence & Talent Development: Promote strong SDLC practices including clean architecture, automated testing, and scalable design. Mentor junior developers through code reviews, technical guidance, and collaborative learning to foster a high-performing engineering culture.
- Lead Automation use cases architecture: Utilize technologies like GenAI, API, D&A, etc., working closely with Product Managers, automation engineers, and stakeholders across business and DTS, and Enterprise Architecture.
- End-to-End Platform Development: Design and maintain front-end (React, TypeScript, Next.js) and back-end (Python, FastAPI, Node.js) layers that support GenAI workflows and agentic interactions.
- API & Agent Framework Integration: Design robust REST and GraphQL APIs for real-time agent communication, tool execution, and memory handling. Integrate frameworks such as LangChain, LangGraph, CrewAI, and vector databases (Pinecone, FAISS, Chroma, Weaviate).
- ETL & Data Pipeline Architecture: Architect and optimize ETL pipelines for real-time and batch data processing. Ensure seamless data flow between GenAI services, databases, and user interfaces.
- Cloud-Native Deployment & Monitoring: Deploy and scale services using Docker, Kubernetes, and CI/CD pipelines. Apply MLOps best practices for monitoring, versioning, and performance tuning of AI components.
- Cross-Functional Collaboration: Collaborate closely with AI researchers, product managers, and delivery teams to align technical execution with strategic goals. Participate in multi-agent orchestration experiments and rapid prototyping cycles.
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