AI Agent Engineer

Momentive•Atlanta, GA

8d•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

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.