> Markdown version of [/jobs/ext/4245028-genai-cloud-engineer](https://www.wearedevelopers.com/jobs/ext/4245028-genai-cloud-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# GenAI Cloud Engineer

- **Company:** Code
- **Location:** Newark, NJ, United States
- **Experience:** Experienced
- **Salary:** $119,912.0 - $129,759.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Business Software, Business Systems, Cloud Engineering, Computer Programming, Distributed Systems, Identity and Access Management, Python (Programming Language), Azure Machine Learning, Salesforce.Com, Search Technologies, Software Engineering, TypeScript, Spring Cloud, Genesys, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, AgentCore, Generative AI, AWS Lambda, Agentic-AI, Fastapi, Event Driven Architecture, AI Platforms, Amazon Bedrock, Api Design, Restful APIs, Data Pipelines, Serverless Computing, Microservices, Agent2Agent Protocol
- **Published:** October 27, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=74f37eb5b40a3857

## About the Role

* 4-7 years of software engineering experience developing cloud-native applications or AI/ML platforms.
 * Strong programming skills in Python and JavaScript/TypeScript.
 * Experience building APIs, microservices, asynchronous processing frameworks, and event-driven architectures.
 * Hands-on AWS experience including Lambda, IAM, EventBridge, container services, serverless technologies, and cloud-native deployment patterns.
 * Experience developing and deploying enterprise GenAI solutions, AI agents, agentic RAG applications, or LLM-powered platforms.
 * Experience with Amazon Bedrock, Bedrock AgentCore, or comparable LLM and agent platforms.
 * Experience with LangChain, LangGraph, MCP, A2A protocols, multi-agent frameworks, or workflow orchestration tools.
 * Strong understanding of prompt engineering, model evaluation, AI quality testing, feedback loops, and reliability measurement.
 * Experience with vector databases, embeddings, semantic retrieval, knowledge bases, and AI data pipelines.
 * Knowledge of AI governance, guardrails, security controls, access management, audit logging, and responsible AI practices., * Experience working in regulated enterprise environments such as financial services, insurance, healthcare, or banking.
 * Experience leading technical workstreams and mentoring engineers.
 * Familiarity with enterprise integrations involving platforms such as Salesforce, Genesys, Service Platforms, and internal business applications.

Technical Environment

 * Python
 * JavaScript / TypeScript
 * AWS Cloud Services
 * AWS Lambda
 * Amazon Bedrock
 * Bedrock AgentCore
 * FastAPI
 * LangChain
 * LangGraph
 * MCP
 * AI Agents
 * Agentic RAG
 * Vector Databases
 * EventBridge
 * Microservices Architecture
 * REST APIs
 * Distributed Systems
 * LLMs
 * Semantic Search
 * Embeddings

## Description

The ideal candidate will have strong AWS cloud engineering experience combined with recent experience developing and deploying GenAI applications, AI agents, LLM-powered solutions, and modern microservices architectures., * Design, develop, and deploy cloud-native applications and AI-powered services using Python and JavaScript/TypeScript.

 * Build and support AI agents, agentic RAG solutions, multi-agent workflows, and conversational AI applications.
 * Develop microservices, APIs, event-driven workflows, and distributed applications within AWS environments.
 * Leverage Amazon Bedrock, Bedrock AgentCore, and related AI services to deliver production-grade AI solutions.
 * Implement retrieval pipelines, vector search, embeddings, semantic search, knowledge bases, and memory frameworks.
 * Integrate AI solutions with enterprise platforms, APIs, business systems, and data sources.
 * Develop orchestration workflows, tool routing, intent classification, guardrails, and human-in-the-loop controls.
 * Build observability, monitoring, tracing, evaluation, and quality assurance capabilities for AI applications.
 * Ensure security, governance, responsible AI practices, auditability, scalability, and operational reliability.
 * Collaborate closely with architects, data engineers, product teams, and business stakeholders to deliver enterprise AI initiatives.