> Markdown version of [/jobs/ext/3217743-ai-engineer](https://www.wearedevelopers.com/jobs/ext/3217743-ai-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 Engineer - **Company:** PERCIENT INC. - **Location:** United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Continuous Integration, Github, Python (Programming Language), PostgreSQL, Query Optimization, SQL Databases, Systems Integration, Data Logging, Sql Optimization, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Backend, Apache Kafka, Machine Learning Operations, Virtual Agents, Automation Anywhere - **Published:** September 24, 2026 - **Apply:** https://www.dice.com/job-detail/21628e83-7cc6-476f-bd91-ebe1769f1608 ## About the Role Advanced Prompt Engineering, Context Engineering skills, Python skills. Hands-on experience building agentic AI solutionsusing Google ADK + LangChain/LangGraph, including orchestration and tool usage patterns, Kafka, Gepa Optimizer, Agent Harness Strong Python development skills for backend services, workflow engines, and AI pipelines. Advanced SQL/PostgresSQL proficiency(complex joins, window functions, query optimization). Experience with RAG architectures and integrating LLMs with enterprise data sources (vector stores + relational systems). Production-grade engineering practices: testing, CI/CD, logging, monitoring, and error handling, Github, Copilot Prompt engineering / Prompt finetuning Mandatory Key Skills 1. Python - Strong hands-on development experience 2. Agentic AI - Hands-on experience building AI agents and agentic workflows 3. Google ADK (Agent Development Kit) 4. LangChain / LangGraph - Agent orchestration, workflows, state management and tool calling 5. Advanced Prompt Engineering & Context Engineering 6. RAG (Retrieval-Augmented Generation) - Enterprise-grade RAG implementation 7. Vector Databases / Vector Stores - Integration with LLM/RAG solutions 8. SQL / PostgreSQL - Complex joins, window functions and query optimization 9. Kafka - Integration with event-driven/backend AI workflows 10. Production Engineering - Testing, CI/CD, logging, monitoring and error handling