> Markdown version of [/jobs/ext/3964553-ai-llm-engineer](https://www.wearedevelopers.com/jobs/ext/3964553-ai-llm-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). --- # AI/LLM Engineer - **Company:** Acestack Llc - **Location:** New York, NY, United States - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Microsoft Azure, Code Review, Databases, Continuous Integration, Software Debugging, Distributed Computing Environment, Python (Programming Language), NoSQL, OpenAI, Search Technologies, Software Construction, Software Deployment, Software Engineering, SQL Databases, Systems Integration, Software Organization, Data Logging, Enterprise Software Applications, Cloud Platform System, Data Ingestion, ReactJS, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Reliability of Systems, Generative AI, Agentic-AI, Git, Fastapi, Kubernetes, Data Management, Machine Learning Operations, Evaluation of Large Language Models, Api Design, Google Gemini, Restful APIs, Model Context Protocol, Software Version Control, Data Pipelines, Docker - **Published:** October 27, 2026 - **Apply:** https://www.careerjet.com/jobad/us666ceef3fe459f93412ee51e592e4fb2 ## About the Role * Strong hands-on experience with Python and software application development. * Experience building LLM applications and agent-based systems. * Proficiency in LangChain, LangGraph, or comparable agent frameworks. * Understanding of agentic AI patterns, including ReAct, planning, reasoning, and autonomous execution. * Experience with MCP (Model Context Protocol), tool calling, memory, and context management. * Experience integrating LLM APIs such as OpenAI, Anthropic, or Google Gemini. * Knowledge of RAG, embeddings, vector databases, and semantic search. * Experience developing APIs and integrating AI applications with external systems. * Understanding of scalable application architecture, testing, and debugging. * Familiarity with Git and modern software development practices., * Experience with multi-agent orchestration and advanced agentic workflows. * Experience with cloud platforms such as AWS, Azure, or GCP. * Knowledge of Docker, Kubernetes, CI/CD, and production deployment. * Experience with LLM evaluation, observability, guardrails, and prompt optimization. * Familiarity with SQL, NoSQL databases, and distributed data processing. * Experience deploying enterprise-grade AI solutions in production environment ## Description We are looking for a highly skilled AI/LLM Engineer to design, develop, and deploy intelligent agent-based systems capable of reasoning, planning, and executing tasks autonomously. The ideal candidate will have strong Python engineering skills, hands-on experience with modern AI agent frameworks, and a solid understanding of LLM architectures, data platforms, and scalable software systems. The candidate will build advanced AI solutions using Large Language Models (LLMs), agentic workflows, tool integration, and context management to deliver reliable, scalable, and production-ready AI applications., 1. Agent & AI System Development * Design, develop, and deploy agent-based systems using frameworks such as LangChain, LangGraph, or similar technologies. * Build intelligent components capable of reasoning, planning, decision-making, and autonomous task execution. * Implement advanced agentic patterns, including ReAct (Reasoning + Acting), multi-step reasoning, and workflow orchestration. * Develop multi-agent systems to coordinate complex tasks and business processes. 2. LLM Capabilities & Enhancements * Integrate LLMs into enterprise applications using APIs from OpenAI, Anthropic, Google, or similar providers. * Develop memory, tool usage, and context management capabilities, including Model Context Protocol (MCP). * Implement tool calling, prompt chaining, and orchestration of LLM-powered workflows. * Optimize prompts and model interactions for accuracy, reliability, latency, and cost. * Implement appropriate evaluation and validation mechanisms to improve LLM output quality. 3. Python Engineering & Application Development * Develop clean, maintainable, and scalable Python applications for AI-driven solutions. * Build REST APIs and integrate AI agents with databases, enterprise systems, and external services. * Implement asynchronous processing, error handling, logging, and testing for production-grade AI applications. * Follow software engineering best practices, including version control, code reviews, and automated testing. 4. Data Platforms & Scalable Architecture * Integrate AI agents with structured and unstructured data sources. * Work with vector databases, embeddings, semantic search, and Retrieval-Augmented Generation (RAG). * Design scalable architectures for LLM applications, including data ingestion, retrieval, and processing pipelines. * Apply security, access control, and responsible AI practices when handling enterprise data. 5. Deployment & Production Support * Deploy and maintain AI/LLM applications in cloud environments such as AWS, Azure, or GCP. * Use Docker, Kubernetes, and CI/CD pipelines where applicable. * Monitor agent performance, troubleshoot production issues, and continuously improve system reliability. * Collaborate with data engineers, software developers, product managers, and business stakeholders.