AI Full Stack Developer

Saxon Global•Richardson, TX

About The Position

The Senior AI Engineer designs, builds, integrates, evaluates, and continuously improves production-grade AI-enabled capabilities that support Client's priority AI use cases. This role is focused on applied AI engineering rather than research alone: taking LLM-powered experiences, agent workflows, RAG patterns, tool/API integrations, evaluation assets, and context-management approaches from concept and prototype into reliable production implementation. The Senior AI Engineer partners closely with product managers, software engineers, AI architects, data product owners, ontology and knowledge teams, quality engineering, design, journey, clinical, operational, privacy, security, and compliance stakeholders. The role translates customer jobs-to-be-done, workflow requirements, trusted data and knowledge sources, and enterprise architecture patterns into secure, observable, measurable, and maintainable AI capabilities. This role is expected to bring strong software engineering fundamentals, hands-on LLM application experience, and practical judgment about how to make AI systems useful, grounded, evaluated, cost-aware, and safe enough for real workflows.

Requirements

Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, Mathematics, or related technical field. Equivalent combination of education and relevant technical experience may be considered.
6+ years of production software engineering, applied AI engineering, ML engineering, data-intensive application development, backend platform engineering, or related technology experience.
Meaningful hands-on experience building LLM-powered applications, AI agents, RAG workflows, prompt/context systems, evaluation assets, AI-assisted workflows, or comparable applied AI capabilities.
Experience designing and deploying end-to-end technical capabilities from requirements and prototypes through production implementation, monitoring, and iteration.
Experience working with product, design, engineering, data, analytics, quality, security, privacy, and business stakeholders to ship reliable software or AI-enabled product features.
Strong programming skills, preferably Python, with backend engineering experience across services, APIs, integrations, data flows, and production application components.
Hands-on familiarity with LLM application development, including prompt engineering, RAG pipelines, embeddings, vector or hybrid retrieval, tool calling, structured outputs, and evaluation methods.
Familiarity with agentic systems and orchestration patterns, including multi-step workflows, state management, memory, routing, decision logic, human-in-the-loop controls, and emerging frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, Google ADK, OpenAI Agents SDK, or comparable tools.
Understanding of cloud and production engineering practices, including containers, CI/CD, monitoring, logging, deployment, security, privacy, latency, cost, reliability, and operational support.
Ability to work with data and integration patterns, including APIs, event flows, data products, enterprise systems, searchable knowledge sources, and scalable architectures.
Ability to test and evaluate AI-enabled systems, including response quality, groundedness, source attribution, task completion, hallucination risk, latency, cost, safety, and reliability.

Nice To Haves

Healthcare, life sciences, financial services, or other regulated-industry experience preferred but not required.
Experience shipping production AI agents, copilots, assistants, conversational experiences, tool-using AI workflows, or AI-enabled automation products.
Experience with GraphRAG, knowledge graphs, ontology-backed retrieval, enterprise search, context graphs, content grounding, or semantic retrieval patterns.
Experience developing evaluation suites, golden datasets, scoring rubrics, prompt/version comparisons, regression tests, observability dashboards, or quality metrics for LLM or agent systems.
Experience integrating AI systems with enterprise APIs, FHIR/healthcare data, customer-facing applications, CRM/CDP platforms, messaging systems, workflow systems, or internal developer platforms.
Experience with model routing, model selection, inference optimization, fine-tuning, LoRA/PEFT, or adapting models to domain-specific workflows.
Prior experience in AI product teams, AI-native companies, big tech, digital health, or product-driven engineering environments.
Demonstrated ability to translate ambiguous AI capabilities into concrete, reliable, user-facing features with measurable outcomes.

Responsibilities

Design, build, test, and operate AI-enabled product capabilities across backend services, APIs, data flows, agent workflows, and user-facing experiences where appropriate.
Translate product requirements, customer jobs-to-be-done, clinical or operational workflows, and stakeholder feedback into concrete technical designs and working AI capabilities.
Take ownership of implementation quality from prototype through production, including maintainable code, documentation, testing, observability, issue resolution, and iterative improvement.
Partner with product and engineering teams to sequence work into shippable increments that deliver measurable value while managing technical risk.
Build LLM-powered applications, agents, agent components, and multi-step workflows that can use tools, retrieve context, follow defined instructions, and complete targeted tasks reliably.
Define and implement agent behavior, task flow, prompt structures, tool-use patterns, fallback behavior, escalation paths, and workflow control logic.
Develop reusable patterns for agent workflows, tool invocation, state handling, structured outputs, context windows, memory, and human-in-the-loop checkpoints where appropriate.
Diagnose agent failures and improve behavior through prompt refinement, retrieval tuning, workflow redesign, evaluation results, telemetry, and user feedback.
Design and implement retrieval-augmented generation workflows that ground AI outputs in trusted enterprise data, content, knowledge, and source systems.
Partner with data, ontology, knowledge, and engineering teams to define retrieval strategies, source attribution, indexing, chunking, metadata, ranking, traceability, and response-grounding expectations.
Build and tune context pipelines, vector or hybrid retrieval patterns, knowledge integrations, and reusable data-access patterns that support high-quality AI outputs.
Identify reusable retrieval, context, tool, API, and integration patterns that can be shared across multiple AI use cases and product teams.
Build and maintain evaluation assets such as test datasets, golden-answer sets, scoring rubrics, prompt/version comparisons, regression tests, and quality dashboards for AI-enabled capabilities.
Measure and improve agent and LLM application performance across accuracy, groundedness, task completion, latency, cost, safety, response quality, and workflow reliability.
Use logs, traces, telemetry, user feedback, evaluation harnesses, and production signals to identify failure patterns and improve system behavior over time.
Partner with Quality Engineering and Product teams to ensure AI capabilities meet acceptance criteria, responsible AI expectations, and release-readiness standards.
Integrate AI capabilities with approved APIs, enterprise systems, tools, services, data products, knowledge repositories, and platform capabilities using secure and governed patterns.
Build tool wrappers, action interfaces, integration utilities, and workflow components that allow AI systems to interact with business processes in controlled and auditable ways.
Collaborate with engineering, architecture, security, privacy, and operations teams to ensure integrations follow enterprise standards for authentication, authorization, monitoring, error handling, and supportability.
Design AI-enabled workflows with clear boundaries around what the system can retrieve, recommend, automate, escalate, or hand off to a human.
Apply strong software engineering practices to AI systems, including code quality, tests, CI/CD, deployment discipline, monitoring, runbooks, incident follow-up, and operational support.
Optimize AI capabilities for latency, cost, reliability, scalability, maintainability, traceability, and user experience.
Document architecture decisions, implementation patterns, evaluation approaches, known limitations, and operational handoffs so teams can maintain and extend AI capabilities responsibly.
Mentor peers and contribute to shared engineering standards, reusable patterns, code reviews, design reviews, and practical guidance for applied AI delivery.