Lead Gen AI Engineer

Lead Gen AI & Data Engineer Location Charlotte, NC Hybrid Local Candidates Preferred In-Person Interview is Mandatory Please do not submit candidates who are not comfortable attending an onsite interview. Compensation Rate: Negotiable Employment Type W2 Preferred Strictly No C2C Client Confidential Position Overview We are seeking a highly experienced Lead Gen AI & Data Engineer to design, develop, and deploy enterprise-grade Agentic AI solutions powered by Large Language Models (LLMs), advanced data engineering, and scalable cloud-native architectures. This role combines deep expertise in Generative AI, Agentic AI systems, enterprise data engineering, and modern AI orchestration frameworks such as Google ADK, LangChain, and LangGraph. The ideal candidate will lead the end-to-end AI solution lifecycle-from designing intelligent agent workflows and integrating enterprise systems, to deploying scalable, production-ready AI platforms with strong governance, observability, and performance optimization. The candidate will work closely with business stakeholders, operations teams, and engineering organizations to build AI copilots and autonomous AI agents that solve complex enterprise problems. Key Responsibilities Agentic AI Solution Development • Design and develop enterprise-grade Agentic AI systems using: • Google ADK • LangChain • LangGraph • Build multi-agent orchestration frameworks with: • State management • Tool integrations • Context-aware workflows • Structured outputs • Integrate enterprise-approved foundation models such as: • Anthropic • Google Gemini • Other enterprise LLM platforms Enterprise AI & Data Integration • Integrate AI agents with enterprise Systems of Record (SoRs) through: • APIs • Databases • File systems • Streaming platforms • Build scalable ETL/ELT pipelines for structured and unstructured data sources • Engineer reliable data ingestion, transformation, and enrichment pipelines • Enable governed enterprise data access for AI-driven workflows RAG & AI Workflow Engineering • Develop scalable Python-based services for: • Retrieval-Augmented Generation (RAG) • Tool calling • AI memory frameworks • Agent orchestration • Real-time decisioning • Design hybrid retrieval strategies leveraging: • Vector databases • Relational systems • Enterprise knowledge repositories Data Engineering & Analytics • Write and optimize complex SQL queries including: • Advanced joins • Window functions • Query optimization • Ensure data quality, lineage, schema evolution, and governance across enterprise systems • Utilize cloud-native tools and platforms across: • GCP (Preferred) • Azure • AWS Observability, Governance & AI Operations • Implement enterprise-grade: • Monitoring • Logging • Testing • CI/CD pipelines • Error handling • Build observability, evaluation, and guardrails across AI and data layers • Ensure: • Performance optimization • Cost efficiency • Compliance • Security • Responsible AI practices • Implement AI safety controls and governance standards for enterprise AI agents Required Qualifications Experience • 10+ years of overall IT experience • 5+ years of experience in: • Generative AI • AI Data Engineering • Agentic AI solution development Core Technical Skills • Advanced Prompt Engineering & Context Engineering expertise • Strong hands-on Python development experience • Experience building production-grade AI services and workflow engines • Hands-on experience with: • Google ADK • LangChain • LangGraph • Agent orchestration frameworks • Enterprise AI pipelines Data Engineering Expertise • Strong experience with: • ETL/ELT pipeline development • API integrations • Data ingestion pipelines • Data quality & lineage • Schema evolution • Advanced SQL expertise including performance optimization AI & LLM Expertise • Experience with: • RAG architectures • LLM integrations • Prompt engineering & fine-tuning • Enterprise AI workflows • Vector databases & retrieval systems Engineering & DevOps • Experience implementing: • CI/CD pipelines • Monitoring & observability • Production-grade testing frameworks • Logging & tracing systems Desired Skills • Experience with modern data stack tools such as: • dbt • Airflow / Composer • Kafka / PubSub • BigQuery • Snowflake • Experience deploying scalable AI platforms on GCP • Familiarity with: • LLMOps frameworks • Prompt/version management • Evaluation frameworks • Cost optimization • Knowledge of: • Data governance • Security & PII handling • AI guardrails & safety controls