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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Full Stack Engineer - Agentic AI & AWS Bedrock - **Company:** JKV International - **Location:** Atlanta, GA, United States (Remote available) - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Cloud Computing, Computer Programming, Fiddler (Software), Identity and Access Management, Python (Programming Language), Machine Learning, Node.Js, Performance Tuning, Next.js, Software Deployment, Web Application Frameworks, Pinecone, Data Processing, Scripting, LangChain, ReactJS, Evaluation Pipelines, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, Generative AI, Backend, Agentic-AI, Vue.js, Build Management, Pgvector, AI Platforms, Few-Shot Learning, LangSmith, Low Latency, CrewAI, Weaviate, AutoGen, Machine Learning Operations, Front End Software Development, Claude, Virtual Agents, Functional Programming, Api Design, Api Gateway, GPT, Software Version Control, OpenSearch - **Published:** October 14, 2026 - **Apply:** https://www.careerjet.com/jobad/usf4dd75480a7d6fdb15c14abaf6c3dafb ## About the Role Cloud & Infrastructure Hands-on experience with AWS Bedrock, including foundation models, Knowledge Bases, and Agents for Bedrock. Experience with AWS services such as Lambda, S3, IAM, and API Gateway. Agentic AI Development Proven experience building autonomous, tool-calling, and multi-step AI agents. Understanding of ReAct, plan-and-execute patterns, agent memory, context management, and tool integration. Experience with agentic AI frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or AWS Bedrock Agents. Programming & Full Stack Development Strong Python programming skills for AI pipelines, agent logic, data processing, and scripting. Strong Node.js skills for API development, backend services, and agent integration. Working knowledge of React, Next.js, Vue, or another modern front-end framework. Ability to develop end-to-end AI applications spanning front-end interfaces, backend services, and AI orchestration. LLM & SLM Expertise Hands-on experience with LLMs such as Claude, GPT-4, Llama, or Mistral. Familiarity with SLMs such as Phi-3, Gemma, or Mistral 7B. Practical experience with prompt engineering, few-shot learning, RAG, and fine-tuning workflows. Monitoring & Evaluation Experience with Fiddler AI or comparable tools such as LangSmith, Arize, Weights & Biases, or Helicone. Ability to evaluate, monitor, and optimize AI model and agent performance in production environments. Preferred Qualifications Experience with vector databases such as Pinecone, pgvector, OpenSearch, or Weaviate. Familiarity with MLOps practices, including model versioning, CI/CD pipelines, and A/B evaluation. Exposure to multimodal AI models involving vision and language. Experience building and deploying AI products or platforms at enterprise scale. Experience integrating streaming responses, tool calls, and AI agent experiences into front-end applications. AWS certifications related to machine learning or AI are a plus. Ideal Candidate Profile A hands-on engineer who actively writes code, builds AI agents, and resolves production issues. Strong ownership mindset, with the ability to take solutions from concept through deployment and ongoing optimization. Curious about emerging AI models, frameworks, and tools. Strong problem-solving, collaboration, and communication skills. Able to explain complex AI concepts and system behavior to technical and non-technical stakeholders., Work Authorization / W2 Eligibility: Candidates must be eligible to work under a W2 employment arrangement. Work Environment: Enterprise AI engineering and production deployment. Equal Opportunity We are committed to providing equal employment opportunities and fostering an inclusive, collaborative, and professional work environment. Interested candidates: Please submit your updated resume highlighting your hands-on experience with AWS Bedrock, agentic AI frameworks, Python, Node.js, and production-grade LLM applications. ## Description We are seeking a hands-on AI Full Stack Engineer to design, develop, and deploy intelligent AI agents and Large Language Model (LLM)-powered applications for enterprise use cases. The ideal candidate will have strong experience in agentic AI development, AWS Bedrock, Python, Node.js, LLM integration, and modern front-end frameworks. This role involves building production-ready AI solutions across the full technology stack, from backend services and agent orchestration to responsive user interfaces and cloud deployment. The successful candidate will take ownership of AI solutions throughout their lifecycle, from proof of concept to production deployment, monitoring, evaluation, and continuous improvement., Design, develop, and deploy AI agents, including autonomous agents, tool-calling agents, multi-step workflows, and multi-agent orchestration. Build and integrate LLM- and SLM-powered applications using AWS Bedrock and other leading AI platforms. Develop agentic AI solutions using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, and AWS Bedrock Agents. Build scalable backend services, APIs, and agent orchestration layers using Python and Node.js. Develop responsive front-end applications and AI-powered interfaces using React, Next.js, Vue, or similar frameworks. Implement Retrieval-Augmented Generation (RAG), prompt engineering, few-shot learning, and model fine-tuning workflows. Leverage AWS services, including Lambda, S3, IAM, and API Gateway, to build and deploy AI applications. Implement AI model and agent monitoring, observability, and evaluation pipelines using Fiddler AI or comparable tools. Define and track AI performance metrics, including accuracy, latency, hallucination rates, tool-call success, and operational cost. Apply responsible AI principles, model evaluation practices, and agent safety measures. Collaborate with product and engineering teams to translate business requirements into production-ready AI capabilities. Contribute to technical documentation, architecture reviews, and continuous improvement of AI engineering practices.