AI Solution Architect

Role: AI Solution Architect

Location: Chicago, IL, USA_ Onsite _Need Local Only

In-Person Customer interview is required

Mandatory Skills: Generative AI

Other skills: AWS, Machine Learning, Cloud & Data Engineering

Years Of Experience: 11 to 15 Years

Job Description

Role Summary & Objectives

• Translate business automation and efficiency goals into scalable, production-grade AI architectures.
• Lead the design of autonomous multi-agent systems, complex reasoning loops, and tool-use workflows.
• Establish robust guardrails, human-in-the-loop decision controls, and system observability.

Key Responsibilities

1. Architecture Design

Build enterprise Generative and Agentic AI platforms featuring high-performance RAG (Retrieval-Augmented Generation) pipelines and vector database integrations.

1. Agent Orchestration

Define multi-agent collaboration patterns, memory management, and autonomous planning frameworks.

1. Governance & Security

Implement data privacy, compliance, risk mitigation, and evaluation guardrails across all AI touchpoints.

1. Cross-functional Leadership

Guide and mentor engineering teams, run discovery workshops with stakeholders, and define reusable deployment patterns.

Technical Stack & Expertise:

Generative AI & Agentic AI

• Retrieval-Augmented Generation (RAG) pipelines, semantic caching, and context window optimization.
• Function calling, tool use, and structured data extraction schemas.
• Evaluation metrics, tracing, and hallucination reduction guardrails.
• Designing agentic-first workflows and autonomous decision loops.

Multi-Agent Systems

• Multi-agent coordination patterns (supervisor-worker, decentralized collaboration, stateful graphs).
• Frameworks like LangChain, LangGraph, and Bedrock Core Runtime for state and memory management.
• Emerging interoperability standards like Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols.

Data & Vector Technologies

• Vector databases (e.g., Milvus, Amazon Aurora PostgreSQL) for high-speed similarity search.
• Data pipelines and embedding generation workflows using Python, FastAPI, or Apache Spark.

Cloud & Infrastructure

• Cloud-native deployment on AWS services including Amazon Bedrock, Lambda, EKS, SageMaker, S3, RDS, and DocumentDB.
• Containerization and orchestration using Docker and Kubernetes.
• CI/CD pipelines for automated testing of non-deterministic AI outputs.

Observability & Responsible AI

• Observability and logging pipelines for tracking agent token usage, latency, and failure states.
• Responsible AI frameworks, data privacy compliance, and bias mitigation guardrails.

Roles & Responsibilities

• Design and implement enterprise-scale Generative AI and Agentic AI solutions.
• Develop and optimize RAG-based architectures and autonomous agent systems.
• Drive AI governance, security, compliance, and operational excellence.
• Collaborate with business and technical stakeholders to deliver scalable AI solutions.
• Mentor engineering teams and establish reusable architecture and deployment standards.