AI Solutions Architect

Kelly Science Engineering Technology & Telecom

Union City, NJ, United States

10 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Experienced

Experience required

3 years minimum

Working hours

Regular working hours

Job source

www.disabledperson.com

Tech stack

LangGraph Framework Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Amazon S3 Automation of Tests Cloud Engineering Continuous Integration Software Design Patterns DevOps Amazon DynamoDB Identity and Access Management

+32 more

Python (Programming Language) Node.Js OAuth Zero Trust Network Access Amazon Simple Notification Service (SNS) Software Deployment TypeScript AWS Cdk Large Language Models Snowflake Multi-Agent Systems Prompt Engineering State Machines Agentic-AI Cloudformation Servicebus Amazon Relational Database Service Kubernetes Amazon Bedrock Production Code AutoGen Apache Kafka Functional Programming Cloudwatch Api Gateway Model Context Protocol Amazon Simple Queue Service (SQS) Terraform Dynatrace Api Management Human in the Loop Microservices

Job description

Agentic Workflow Development: Architect and build multi-step agentic workflows using AWS Agent Core (or equivalents), integrating advanced tool use, memory preservation, retrieval, and multi-step reasoning capabilities.
API & Connectivity Infrastructure: Design and implement secure, high-throughput API layers using AWS API Gateway and the Model Context Protocol (MCP) to safely expose agent capabilities to internal systems and external consumers.
Core Cloud Engineering: Build and manage the underlying AWS infrastructure supporting your agents, optimizing compute (Lambda, ECS, EKS), event-driven orchestration (Step Functions, EventBridge), storage (S3, DynamoDB, RDS/Aurora, Snowflake), and messaging (SQS, SNS, Kafka).
LLM Integration & Prompt Engineering: Integrate foundation models via Amazon Bedrock (and other endpoints), mastering structured function calling, prompt/tool boundaries, and context window optimization.
Guardrails & Observability: Implement enterprise-grade controls for reliability, safety, and cost control-including distributed tracing (AWS X-Ray), evaluation frameworks, toxic content filtering, and Human-in-the-Loop (HITL) validation checkpoints.
Zero-Trust Security: Design rigid authentication and authorization patterns (IAM, OAuth, Lambda Authorizers) governing how autonomous agents interface with internal microservices and third-party APIs.
Organizational Impact: Establish reusable reference architectures, design patterns, and deployment best practices to accelerate agentic AI development across the entire enterprise.

Requirements

Experience: 5+ years of professional software/solutions engineering experience, including 3+ years of production-grade AWS architecture and development.
Agentic AI Expertise: Demonstrated, hands-on experience building complex, multi-agent workflows or tool-using autonomous systems (beyond basic prompt engineering or short-lived POCs).
Framework Proficiency: Practical experience with AWS Agent Core, or a proven ability to scale quickly into it using related orchestration frameworks (e.g., LangGraph, AutoGen, Bedrock Agents).
Advanced API Management: Deep expertise with AWS API Gateway, including REST/HTTP API configuration, custom authorizers, throttling policies, and request/response transformations.
AWS Systems Mastery: Strong fluency with the core AWS ecosystem, specifically Lambda, Step Functions, DynamoDB, S3, EventBridge, IAM, and CloudWatch.
Modern Engineering Practices: Strong software engineering fundamentals, including API contract design, automated testing, CI/CD pipelines, and Infrastructure as Code (IaC) using Terraform, AWS CDK, or CloudFormation.
Languages: Ability to write clean, maintainable, production-quality code in Python and/or TypeScript (Node.js).

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