Senior Data Scientist
About The Position
The position is listed as Senior Data Scientist, while the overview identifies the position title as Lead AI Engineer. This role owns the end-to-end architecture and technical strategy for AI products and platforms, including model selection, multi-agent and workflow orchestration, and data and retrieval architecture. It evaluates emerging technologies such as next-generation and multimodal models, real-time streaming infrastructure, agent frameworks, and workflow engines. The role designs and leads shared AI services and SDKs, reusable RAG pipelines and ingestion frameworks, AI copilot and agent UI patterns, and modular back-end capabilities. It establishes standards for prompt design and versioning, model and retrieval evaluation, and AI observability, logging, and incident response. The position provides hands-on technical leadership and mentoring to AI Engineers, ML Engineers, and Data Scientists; guides architecture and code quality; and supports a culture of innovation, quality, and responsible AI. It partners with Product, Design, and Business stakeholders to identify valuable AI opportunities, shape roadmaps, set success criteria, and deliver cross-functional AI projects from concept through production. The role defines evaluation frameworks and human evaluation workflows, drives AI governance and responsible AI practices, and works with security, privacy, and legal teams on compliance. It also leads performance, reliability, and cost optimization, establishes service-level targets, and mitigates risks involving scalability, data quality, and vendor lock-in. Remote work is permitted, with travel to the Richardson, Texas office one week per month.
Requirements
- Bachelor’s degree in Computer Science, Machine Learning, Data Science, or a related field.
- Seven years of experience in the job offered or as a Data Scientist, Software Engineer, ML Engineer, or an acceptable alternate occupation.
- Two years of experience in a senior or leadership role.
- Experience with Python and TypeScript or JavaScript in production environments.
- Experience designing and operating distributed, cloud-native systems on platforms such as GCP, Azure, or AWS.
- Experience with containerization and orchestration, such as Docker or Kubernetes, and modern CI/CD.
- Experience working with coding assistants such as Windsurf, Cursor, or Codex.
- Experience architecting and shipping complex AI systems to production at scale.
- Experience leading multi-engineer initiatives and mentoring others.
- Ability to make data-driven tradeoffs between speed, quality, and cost.
- Advanced experience with LLM-based application design, including prompting, tool use, function calling, and multi-agent workflows.
- Advanced experience with RAG architectures, vector databases, and retrieval optimization techniques.
- Advanced experience with AI observability, monitoring, and evaluation frameworks.
- Ability to communicate complex AI concepts to executives and non-technical partners.
- Experience presenting AI strategy and progress to leadership and cross-functional teams.
- Advanced experience in context engineering, including prompt design, RAG pipelines, grounding strategies, and multi-turn orchestration to optimize LLM reasoning.
- Ability to build and maintain LLM evaluation harnesses, including dataset creation, automated benchmarking, regression testing, and output-quality analysis.
- Hands-on experience with LLM fine-tuning, such as SFT, PEFT/LoRA, and distillation.
- Ability to prototype and deliver AI proofs of concept, translating ambiguous business problems into validated technical solutions through rapid experimentation.
- Ability to read and interpret scientific research, extract actionable insights from AI/ML papers, and implement state-of-the-art techniques.
Responsibilities
- Own end-to-end architecture for AI products and platforms, including model selection, multi-agent and workflow orchestration, and data and retrieval architecture.
- Evaluate emerging AI technologies, including next-generation and multimodal models, real-time streaming infrastructure, agent frameworks, and workflow engines.
- Design and lead implementation of shared AI services and SDKs, reusable RAG pipelines and ingestion frameworks, AI copilot and agent UI patterns, and modular agent back-end capabilities.
- Establish standards for prompt design and versioning, model and retrieval evaluation, and AI observability, logging, and incident response.
- Provide hands-on technical leadership to AI Engineers, ML Engineers, and Data Scientists; guide architectural decisions and code quality; conduct design and code reviews; and mentor team members.
- Help build the AI engineering culture, emphasizing innovation, quality, and responsible AI.
- Partner with Product, Design, and Business stakeholders to identify high-value AI use cases, shape product roadmaps, and define measurable success criteria.
- Lead cross-functional AI projects from concept to production, defining requirements and plans, ensuring timely and high-quality delivery, and iterating based on feedback and metrics.
- Define evaluation frameworks using offline and online metrics for relevance, safety, user satisfaction, and business impact, and establish human evaluation workflows for complex or sensitive tasks.
- Drive AI governance and responsible AI practices, including content safety, bias and fairness, PII handling, and compliance with policies and regulations.
- Collaborate with security, privacy, and legal teams to ensure AI solutions are compliant.
- Lead AI system performance and cost optimization, including model routing, distillation, caching, infrastructure sizing, and build-versus-buy decisions.
- Establish SLAs and SLOs for key AI services, including latency, uptime, and error budgets.
- Identify and mitigate technical risks related to scalability, data quality, and vendor lock-in.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
