Lead AI Engineer

Lead AI Engineer Hybrid | Lewisville, TX Permanent Role The client is seeking a Lead AI Engineer to serve as the hands-on technical lead for AI-accelerated engineering within its Data, Analytics & AI organization. This role will develop reusable AI skills, workflows, and standards that can be adopted across engineering teams. The position is a senior individual contributor role with no direct reports, focused on building, operating, and scaling AI-assisted development practices. This is not a data engineering or model-training role. The focus is on AI-enabled software development, agentic workflows, automation, governance, and operational excellence. Responsibilities: Develop and maintain reusable AI skills, prompts, and workflows that align with the client's engineering standards. Design and operate AI-assisted and agentic workflows for developing, refactoring, testing, and supporting data and software products. Establish and manage the AI development lifecycle, including versioning, evaluation, deployment, monitoring, and continuous improvement. Build automated testing, evaluation, and quality gates into AI-assisted development workflows. Define appropriate levels of AI autonomy and implement human review for critical or irreversible actions. Lead adoption of AI engineering practices across development teams and mentor engineers on prompt engineering and effective use of AI. Maintain shared, version-controlled AI capabilities and prioritize requests from engineering teams. Develop AI-driven capabilities that turn platform data and telemetry into actionable insights and recommendations. Enable secure, natural-language access to governed data using semantic layers and trusted data sources. Monitor the reliability, performance, cost, and observability of LLM-driven workflows. Implement guardrails, prompt-injection defenses, data-leakage protections, access controls, and audit trails. Support ML model operations, including versioning, validation, serving, and drift monitoring. Optimize AI workloads through model routing, caching, right-sizing, and token/cost management. Track adoption and measurable improvements in delivery speed, quality, and cost. Required Skills & Qualifications: Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field; master's preferred. 8+ years of experience in software, data, or ML engineering, including recent hands-on experience delivering AI or agentic systems in production. Strong Python and modern software engineering skills with experience building production-grade, tested, and version-controlled applications. Hands-on experience with AI-assisted development, agentic workflows, prompt engineering, and reusable AI skill development. Experience with LLM/agentic technologies such as LangChain, LangGraph, LlamaIndex, MCP, retrieval, and vector databases. Experience with model serving, orchestration, evaluation, and observability tools such as MLflow, vLLM, Langfuse, Phoenix, or OpenTelemetry. Experience with containerization and orchestration technologies such as Docker and Kubernetes, as well as API frameworks such as FastAPI. Knowledge of model, inference, prompt, and context optimization, including caching, batching, model routing, quantization, and latency/throughput optimization. Experience managing AI and data workload costs, including token budgeting, compute rightsizing, cost monitoring, and FinOps practices. Experience deploying and operating AI/LLM workflows end to end, including CI/CD, testing, evaluation, monitoring, and human review controls. Strong understanding of AI governance, security, data privacy, and production reliability. Demonstrated technical leadership and mentoring experience across multiple engineering teams. Strong communication, ownership, problem-solving, and ability to work effectively in an ambiguous environment. Preferred Experience: Experience with Snowflake, Azure, Cortex, Snowpark ML, or Snowpark Container Services. Experience with dbt, Coalesce, Apache Airflow, Apache Spark, or Apache Iceberg. Experience operating open-weight or self-hosted models alongside managed AI services. Experience building reusable AI skill libraries and engineering standards in Git. Experience implementing AI governance and security controls in an enterprise or regulated environment. Familiarity with PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, Ray, or Triton. Experience building internal AI platforms or enablement capabilities used across multiple engineering teams. Show more Show less