AI Solution Architect

Job Title: AI Solution Architect
Location: Chicago, IL, USA
Duration:12+ Months
Visa Independent Candidates

 
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.
2. Agent Orchestration
Define multi-agent collaboration patterns, memory management, and autonomous planning frameworks.
3. Governance & Security
Implement data privacy, compliance, risk mitigation, and evaluation guardrails across all AI touchpoints.
4. 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.