We are looking for an AI Agent Engineer to design, build, and deploy production-grade agentic systems on AWS. This role focuses on multi-agent workflows, tool integrations, retrieval-augmented generation, orchestration, memory, observability, and secure enterprise deployment. The ideal candidate has strong software engineering depth, cloud architecture experience, and hands-on experience building AI/ML platforms and distributed systems.
This is a Hybrid role in Atlanta, GA.
Key Responsibilities
· Design and implement AI agents and multi-agent systems for enterprise use cases using modern agentic design patterns.
· Build agent orchestration frameworks with support for tool use, workflow control, context handling, and memory.
· Develop RAG-based applications and integrate enterprise data sources, APIs, and analytical systems.
· Build secure, scalable agent platforms on AWS using containerized and serverless architectures.
· Implement observability, telemetry, logging, and evaluation frameworks for agent performance and reliability.
· Integrate agents with structured and unstructured data systems, including data lakes, event streams, and operational databases.
· Collaborate with data scientists, ML engineers, product managers, and platform teams to productionize AI solutions.
· Define reusable architecture patterns, standards, and best practices for agent development across teams.
· Evaluate emerging AI frameworks, protocols, and orchestration approaches for production adoption.
Required Qualifications
· Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
· 7+ years in software engineering, cloud architecture, or distributed systems.
· 3+ years building AI/ML-enabled applications in production.
· Strong Python and Java or Go experience.
· Strong AWS experience with services such as EKS, ECS/Fargate, Lambda, SQS, SNS, Athena, SageMaker, API Gateway, and CDK/CloudFormation/Terraform.
· Experience designing microservices and event-driven systems.
· Experience integrating LLMs, RAG pipelines, vector retrieval, or agent frameworks into business applications.
· Familiarity with Kubernetes, REST APIs, CI/CD, and infrastructure as code.
· Strong understanding of enterprise security, observability, and scalable system design.
Preferred Qualifications
· Experience with agent frameworks such as LangGraph or similar orchestration tools.
· Experience with Model Context Protocol, agent-to-agent communication, or multi-agent architectures.
· Experience with streaming systems, workflow engines, and analytical data platforms.
· Exposure to GCP or Azure in addition to AWS.
· Experience in fraud, identity, operations research, or enterprise analytics use cases.
What Success Looks Like
· Production-ready AI agents that automate complex workflows safely and reliably.
· Reusable agent platform components that reduce time-to-delivery across teams.
· High-quality telemetry, monitoring, and evaluation for agent performance.
· Strong alignment between AI capabilities and measurable business outcomes.
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