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
- Architecture Design
Build enterprise Generative and Agentic AI platforms featuring high-performance RAG (Retrieval-Augmented Generation) pipelines and vector database integrations.
- Agent Orchestration
Define multi-agent collaboration patterns, memory management, and autonomous planning frameworks.
- Governance & Security
Implement data privacy, compliance, risk mitigation, and evaluation guardrails across all AI touchpoints.
- 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.
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