AI Full Stack Developer

Saxon Global•Dallas, TX

16h•Hybrid

About The Position

We are seeking an experienced AI Full Stack Developer to lead the technical strategy and architecture for our AI products and platforms. This role involves owning the end-to-end architecture, designing and implementing shared AI services, and providing technical leadership to a team of engineers and data scientists. You will partner closely with Product, Design, and Business stakeholders to identify AI use cases, shape product roadmaps, and lead complex AI projects from concept to production. A key aspect of this role is defining robust evaluation frameworks, driving AI governance and responsible AI practices, and managing the performance, reliability, and cost of AI systems. The ideal candidate will have a strong background in software engineering, ML engineering, or data science, with hands-on experience in applied AI/LLMs and a proven track record of shipping complex AI systems to production at scale.

Requirements

Typically 5 + years of experience in Software Engineering, ML Engineering, or Data Science, with 1+ years hands-on in Applied AI/LLMs.
Deep expertise in Python and TypeScript/JavaScript in production environments.
Deep expertise in designing and operating distributed, cloud-native systems (GCP, Azure, or AWS).
Deep expertise in containerization and orchestration (Docker, Kubernetes) and modern CI/CD.
Proven track record of architecting and shipping complex AI systems to production at scale.
Proven track record of leading multi-engineer initiatives and mentoring others.
Proven track record of making data-driven tradeoffs between speed, quality, and cost.
Advanced experience with LLM-based application design (prompting, tool use, function calling, multi-agent workflows).
Advanced experience with RAG architectures, vector databases, and retrieval optimization techniques.
Advanced experience with AI observability, monitoring, and evaluation frameworks.
Excellent communication and stakeholder management skills.
Ability to communicate complex AI concepts to executives and non-technical partners.
Comfortable representing AI strategy and progress to leadership and cross-functional teams.

Nice To Haves

Experience with coding assistants like Windsurf, Cursor, Codex, etc.
Experience with multimodal and real-time agents (voice + text + UI control, streaming interactions).
Background in AI experiment tracking and evaluation frameworks (e.g., OpenAI Evals, Langsmith Evals, etc.).
Background in data platforms (data lakes/warehouses, feature stores, event streams like Kafka).
Background in browser automation software such as PlayWright.
Experience designing AI products in domains with strong regulatory or privacy constraints.
Experience building organizational AI strategies, setting standards, and helping define AI hiring and capability roadmaps.

Responsibilities

Own the end-to-end architecture for AI products and platforms, including model selection strategy, multi-agent and workflow orchestration patterns, and data and retrieval architecture.
Evaluate and introduce emerging technologies such as next-generation LLMs, multimodal models, real-time streaming infrastructures, and advanced agent frameworks.
Design and lead the implementation of shared AI services and SDKs, including reusable RAG pipelines, common UI components for AI copilots, and modular backend processes.
Establish standards and best practices for prompt design, model and retrieval evaluation, and observability for AI systems.
Provide hands-on technical leadership to AI Engineers, ML Engineers, and Data Scientists, guiding architectural decisions and code quality.
Conduct thorough design and code reviews, and mentor team members in LLMs, RAG, agentic design, and production AI practices.
Help define and grow the AI engineering culture, focusing on innovation, quality, and responsible AI.
Partner closely with Product, Design, and Business stakeholders to identify high-value AI use cases and shape product roadmaps.
Lead complex, cross-functional AI projects from concept to production, ensuring clear requirement definitions, on-time delivery, and ongoing iteration.
Define robust evaluation frameworks for AI systems, including offline and online metrics and human evaluation workflows.
Drive AI governance and responsible AI practices, addressing content safety, bias, fairness, PII handling, and regulatory compliance.
Collaborate with security, privacy, and legal teams to ensure compliant AI solutions.
Lead performance and cost optimization for AI systems, including model routing, infrastructure right-sizing, and build-vs-buy decisions.
Establish SLAs/SLOs for key AI services and proactively identify and mitigate technical risks.