Overview
On Site
Accepts corp to corp applications
Contract - W2
Contract - 12 month(s)
Skills
Langgraph
AI Agents
Context Engineering
Job Details
Job : LLM/Prompt-Contest Engineer - Full Stack Python (AI Agents, LangGraph, Context Engineering)
Location - 1st Atlanta, 2nd Dallas, Srd Seattle (Onsite)
About the Role:
We are looking for a highly skilled LLM/Prompt-Context Engineer with a strong fellack Python background to deriga, develop, and integrate intelligent systems focused on large language models (LLMs), prompt engineering, and advanced contert management. In this role, you will play a critical part in architecting context-rich Al solutions, crafting effective prompts, and ensuring seamless agent interactions using frameworks like LangGraph Key Responsibilities:
- Prompt & Context Engineering:
Design, optimize, and evaluate prompts for LLMs to achieve precise, reliable, and contextually relevant outputs across a variety of use cases. - Context Management:
Architect and implement dynamic context management strategies, including session memory, retrieval-augmented generation, and user personalization, to enhance agent performance. - LEM Integration:
Integrate, fine-tune, and orchestrate LL Ms within Python-based applications, leveraging APIs and custom pipelines for scalable deployment. - LangGraph & Agent Flows:
Build and manage complex conversational and agent workflows using the LangGraph framework to support muit-agent or mult step solutions. - Eullstack Derelopment:
Develop robust backend services, APIs, and (optionally) front-end interfaces to enable end-to-end Al-powered applications. - Collaboration:
Work closely with product, data science, and engineering teams to define requirements, run prompt experiments, and iterate quickly on solutions. - Evaluation & Optimization:
Implement testing, montoring, and evaluation pipelines to continuously improve prompt effectiveness and contest handling:
Required Skills & Qualifications:
- Deep experience with fullstack, Python development (FastAPL Flask, Django; SQLNoSQL databases).
- Demonstrated expertise in prompt engineering for LIMs (e g., OpenAI, Anthropie, open-source LL Ms).
Strong understanding of context engineering, including session management, vector search, and knowledge retrieval strategies Hands-on experience integrating Al agents and LLMs into production systems Proficient with conversational flow frameworks such as LangGraph Familiarity with cloud infrastructure, containerization (Docker), and CL CD practices. - Exceptional analytical, problem-solving and communication skills.
Preferred:
Experience evaluating and fine-tuming LLMs of working with RAG architectures.
- Background in information retrieval, pearch, or knowledge management systems
- Contributions to open-source LLM, agent, or prompt engineering projects.
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