Lead AI Engineer

RedStream Technology; Lewisville (TX); On-site; USD 180,000 - 240,000; Full time

Job summary

RedStream Technology seeks a Lead AI Engineer to act as the hands-on technical lead for AI-accelerated engineering within its Data, Analytics & AI organization. The role focuses on AI-enabled software development, agentic workflows, automation, governance, and operational excellence.

The position is a senior individual contributor role with no direct reports, emphasizing building, operating, and scaling AI-assisted development practices across engineering teams.

Qualifications

• Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or related field.
• 8+ years of experience in software, data, or ML engineering, including production AI delivery.
• Strong Python and modern software engineering skills with production-grade, tested, version-controlled apps.
• 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 Docker and Kubernetes, and API frameworks like FastAPI.
• Knowledge of model, inference, prompt, and context optimization including caching, batching, and latency optimization.

Responsibilities

• Develop and maintain reusable AI skills, prompts, and workflows aligned with 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 AI autonomy levels and implement human review for critical actions.
• Lead adoption of AI engineering practices across teams and mentor engineers.
• Maintain shared AI capabilities and prioritize requests from engineering teams.
• Develop AI-driven capabilities turning platform data into actionable insights.
• Enable secure, natural-language access to governed data via semantic layers.
• Monitor 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 routing, caching, right-sizing, and token management.
• Track adoption and measurable improvements in delivery speed and quality.

Skills

Python

LLM tech

Prompt engineering

Production-grade software

Docker

Kubernetes

LangChain

PyTorch

OpenTelemetry

LangChain/LangGraph

Education

Bachelor's degree in CS/Data/AI

Master's preferred

Tools

Docker

Kubernetes

FastAPI

MLflow

vLLM

Langfuse

Phoenix

OpenTelemetry

Job description

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.