AI Architect

ClientOur client is a huge investment company headquartered in New York City.The working time zone will be: Latam/US/Canada (GMT-4) — GMT -5Project overviewThe primary objective of this exciting project is to enhance the functionality of a cutting-edge Data Platform, empowering business users with the insights they need to make data-driven investment decisions. The Data Platform is already in production, and we are developing new features and projects tailored to specific business data-driven needs as well as introducing ongoing architectural changes to increase usage efficiency.Position overviewWe are seeking an experienced AI Architect to define, design, and lead the architecture of an Agentic AI solution that enables natural language interaction with enterprise data platforms.The AI Architect will be responsible for shaping the overall AI strategy, designing scalable and secure architectures, and guiding development teams in implementing advanced AI and NLP capabilities. This role requires a strong balance of hands-on technical expertise, architectural leadership, and business alignment.Technology stackAzure AI Services / Azure AI FoundryAgentic AI FrameworksPythonSnowflake (including Snowflake Cortex)Enterprise Data PlatformsCloud-native architectures (Azure)ResponsibilitiesDesign and own the overall AI/ML architecture, ensuring scalability, reliability, and maintainabilityDefine standards and best practices for model development, deployment, monitoring, and governanceLead the selection of AI technologies, frameworks, tools, and cloud servicesArchitect end-to-end AI solutions, from data ingestion and model training to inference and integrationCollaborate with product managers and stakeholders to translate business requirements into technical AI solutionsGuide engineering and data science teams on architectural decisions and implementation approachesEnsure AI solutions meet security, compliance, and ethical AI requirementsEvaluate emerging AI trends and technologies and recommend adoption where appropriateSupport performance optimization, cost efficiency, and model lifecycle managementRequirementsStrong experience designing and implementing AI/ML systems in productionExperience with cloud platforms (AWS, GCP, or Azure) and AI/ML servicesHands-on experience with MLOps practices (CI/CD, model versioning, monitoring, retraining)Strong system design and architectural thinkingExperience with Azure AI FoundryAbility to communicate complex technical concepts to both technical and non-technical stakeholdersHands-on experience with multi-agent AI frameworks (e.g., LangChain, LangGraph, LlamaIndex, LangFlow, Strands Agents)Solid knowledge of Python testing frameworks (unittest, pytest, testcontainers) and load testing tools (Locust)Proven experience with retrieval-augmented generation (RAG) and AgenticRAG architecturesFamiliarity with major cloud-based AI services and model integration pipelinesExperience working with vector databases and knowledge graphsUnderstanding of model tokenization, cost optimization, and inference scalingFluent English (spoken and written)