AI Architect - US/Canada (GMT-7) time zone

Client

Our client is a huge investment company headquartered in New York City.

Project overview

The 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 overview

We 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 stack

Azure AI Services / Azure AI FoundryAgentic AI FrameworksPythonSnowflake (including Snowflake Cortex)Enterprise Data PlatformsCloud-native architectures (Azure)

Responsibilities

• Design and own the overall AI/ML architecture, ensuring scalability, reliability, and maintainability

• Define standards and best practices for model development, deployment, monitoring, and governance

• Lead the selection of AI technologies, frameworks, tools, and cloud services

• Architect end-to-end AI solutions, from data ingestion and model training to inference and integration

• Collaborate with product managers and stakeholders to translate business requirements into technical AI solutions

• Guide engineering and data science teams on architectural decisions and implementation approaches

• Ensure AI solutions meet security, compliance, and ethical AI requirements

• Evaluate emerging AI trends and technologies and recommend adoption where appropriate

• Support performance optimization, cost efficiency, and model lifecycle management

Requirements

• Strong experience designing and implementing AI/ML systems in production

• Experience with cloud platforms (AWS, GCP, or Azure) and AI/ML services

• Hands-on experience with MLOps practices (CI/CD, model versioning, monitoring, retraining)

• Strong system design and architectural thinking

• Experience with Azure AI Foundry

• Ability to communicate complex technical concepts to both technical and non-technical stakeholders

• Hands-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 architectures

• Familiarity with major cloud-based AI services and model integration pipelines

• Experience working with vector databases and knowledge graphs

• Understanding of model tokenization, cost optimization, and inference scaling

• Fluent English (spoken and written)