AI Solution Architect

TEKFORTUNE INC; Chicago (IL); On-site; USD 180,000 - 240,000; Full time

Job summary

TEKFORTUNE INC in Chicago seeks a senior AI engineering lead to translate business automation goals into scalable AI architectures and to design autonomous multi-agent systems with robust guardrails and observability.

You will drive enterprise Generative AI platforms, agent orchestration, and governance across teams, mentoring engineers and defining reusable deployment patterns to ensure scalable, secure AI solutions.

Qualifications

• 11 to 15 years of experience in AI systems and software architecture.
• Strong background in Generative AI, agents and multi-agent orchestration.

Responsibilities

• Translate business automation goals into scalable AI architectures.
• Lead the design of autonomous multi-agent systems and tool-use workflows.
• Establish guardrails, human-in-the-loop controls, and system observability.
• Build enterprise Generative and Agentic AI platforms with high-performance RAG pipelines.
• Define multi-agent collaboration patterns, memory management, and autonomous planning.

Job description

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

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

• 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-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.
• 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.