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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer - Agentic AI (Lead GenAI & Data Engineer) - **Company:** THE JUDGE GROUP, INC. - **Location:** Charlotte, NC, United States - **Experience:** Expert - **Salary:** $143,520.0 - $153,920.0 - **Contract:** Temporary contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, BigQuery, Databases, Continuous Integration, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Security, Memory Management, Python (Programming Language), Query Optimization, Software Safety, Software Deployment, Software Engineering, Data Streaming, Systems Integration, Workflow Management Systems, Data Logging, Freeform SQL, Google Cloud, Sql Optimization, Large Language Models, Snowflake, Multi-Agent Systems, Prompt Engineering, Backend, Apache Kafka, Machine Learning Operations, Feature Extraction, Virtual Agents, Software Version Control, Data Pipelines - **Published:** May 13, 2026 - **Apply:** https://www.dice.com/job-detail/949bbf33-5460-46f1-9a06-f76f7683e126 ## About the Role * 5+ years of Software Engineering experience, or equivalent demonstrated through a combination of work experience, consulting, education, training, or military experience. * 5+ years of hands-on experience in GenAI, Agentic AI, and AI/Data Engineering roles. * Strong proficiency in Python for backend services, AI pipelines, and workflow orchestration. * Hands-on experience building agentic AI solutions using Google ADK and LangChain/LangGraph. * Advanced prompt engineering and context engineering skills. * Solid data engineering background, including: + Building ETL/ELT pipelines + Integrating data from APIs, databases, files, and streaming sources + Managing data quality, schema evolution, and lineage * Advanced SQL skills (complex joins, window functions, query optimization). * Experience implementing RAG architectures and integrating LLMs with enterprise data sources (vector stores and relational systems). * Experience delivering production-grade systems, including testing, CI/CD, logging, monitoring, and error handling. * Ability to consult on complex, large-scale initiatives, evaluate ambiguous problems, and collaborate strategically with cross-functional stakeholders. Preferred Qualifications * Experience with modern data stack tools such as dbt, Airflow/Cloud Composer, Kafka/Pub/Sub, BigQuery, or Snowflake. * Familiarity with vector databases and hybrid retrieval strategies. * Experience deploying scalable solutions on Google Cloud Platform (preferred). * Knowledge of data governance, security, and PII handling within AI and data pipelines. * Experience with LLMOps practices, including evaluation frameworks, prompt/version management, tracing, and cost optimization. * Experience implementing AI safety, guardrails, and risk controls in enterprise environments. ## Description We are seeking a Lead GenAI & Data Engineer to design, build, and scale intelligent, enterprise-grade Agentic AI systems. In this role, you will work hands-on with Google Agent Development Kit (ADK) and LangChain/LangGraph to create AI agents that integrate deeply with enterprise Systems of Record (SoRs) through reliable, governed data pipelines. You will lead the end-to-end delivery of AI agent solutions-from architecture and workflow design to production deployment-while ensuring performance, observability, security, and compliance. This role requires strong collaboration with business, operations, and process excellence teams to deliver real-world AI copilots and agents with measurable business impact., * Design and develop agentic AI applications using Google ADK, LangChain, and LangGraph, including: + Multi-agent orchestration + State and memory management + Tool integration using enterprise-approved LLMs * Integrate AI agents with enterprise Systems of Record by building and maintaining secure APIs, connectors, and data pipelines across structured and unstructured data sources. * Incorporate organization-approved foundation models (e.g., Google Gemini, Anthropic) into task-oriented, agent-based workflows. * Collaborate with Process Excellence, Operations, and business partners to identify, prototype, and implement AI agents and copilots. * Build scalable, production-ready Python services supporting agent workflows, including RAG, tool calling, structured outputs, and memory. * Engineer batch and streaming data pipelines to provide governed, high-quality data access for AI systems. * Develop and optimize advanced SQL queries for analytics, feature extraction, and real-time agent decisioning. * Implement observability, evaluation, guardrails, and cost controls across data and AI layers to ensure quality, reliability, compliance, and efficiency. * Apply cloud-native tools and best practices (Google Cloud Platform preferred; Azure/AWS acceptable) to ensure data quality, lineage, and secure operations. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Should we build Generative AI into our existing software?](https://www.wearedevelopers.com/videos/1129-should-we-build-generative-ai-into-our-existing-software) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [Making Data Warehouses fast. A developer's story.](https://www.wearedevelopers.com/videos/302-making-data-warehouses-fast-a-developer-s-story) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [The Prompt Engineer ✍️](https://www.wearedevelopers.com/magazine/216-the-prompt-engineer) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)