Applied AI Engineer
About The Position
The Applied AI Engineer applies expertise in generative AI application development, agent orchestration, retrieval architecture, and evaluation methodology to deliver production AI solutions. The role follows established AI governance, security review, and evaluation processes to deploy reliable, auditable systems in a regulated environment, balancing delivery speed with required governance and review gates. The engineer translates business problems into scoped technical roadmaps, identifies constraints before development, designs and builds agentic systems and production RAG pipelines, and provides secure access to enterprise tools and data through connectors. The role also deploys and operates applications on managed cloud platforms, builds evaluation and observability capabilities, improves deployed applications, and establishes reusable patterns and reference architecture. The position works directly with non-technical business owners and keeps current with evolving LLM capabilities and AI development practices. The position follows a hybrid schedule in Taylor, Texas, two days per week. Expected salary range: $149,000–$205,000.
Requirements
- At least 5 years of job-related work experience in AI/ML or software engineering, in excess of degree requirements.
- Proven record of building and deploying production-grade autonomous agents, rather than prototypes.
- Experience with agent orchestration frameworks such as LangGraph, Microsoft Agent Framework, or comparable frameworks.
- Experience with production RAG, vector search, and vector databases such as pgvector, Azure AI Search, or Databricks Vector Search.
- Strong Python skills and hands-on LLM API integration experience.
- System-design fundamentals, including scalable, reliable, maintainable services; API and integration-boundary design; and trade-offs involving latency, throughput, and cost.
- Experience building or extending tool and data connectors for LLM applications.
- Experience deploying and operating applications on a managed cloud platform.
- Knowledge of AI governance, model lifecycle, and evaluation methodology.
- Stakeholder and discovery skills, including working directly with non-technical business owners, scoping ambiguity, and operating autonomously.
- Bachelor’s degree in Computer Science, Data Science, Information Systems, Engineering, or a related field; alternatively, equivalent knowledge provided by a combination of education and experience.
Nice To Haves
- Solution and system architecture across multiple applications, including security-by-design and reference architecture.
- Experience with large-scale data platforms such as Databricks for retrieval, feature, or pipeline work.
- Experience in a regulated industry such as energy, finance, or healthcare, or in an audit-driven environment.
- Experience with multi-agent orchestration and context engineering.
- Cloud or AI/ML certification, such as Azure AI Engineer, AWS Machine Learning, or Databricks.
Responsibilities
- Translate ambiguous business problems into scoped technical roadmaps and identify data-access, compliance, latency, and cost constraints before development begins.
- Design and build production agentic systems that support planning, tool calling, multi-step reasoning, memory, and error recovery using orchestration frameworks such as LangGraph or Microsoft Agent Framework.
- Implement production RAG pipelines, including chunking, embeddings, hybrid search, reranking, retrieval-quality evaluation, and content freshness.
- Build and extend connectors that provide agents secure, standardized access to enterprise tools and data.
- Deploy applications on managed cloud platforms and integrate them with enterprise systems and collaboration tools.
- Build evaluation suites, tracing, and rollback paths to support reliable production agents.
- Monitor, debug, and improve deployed applications against evaluation metrics.
- Define application architecture, data flows, and integration boundaries, designing for scalability, reliability, latency, and cost.
- Turn successful implementations into reusable components and reference architecture.
- Work directly with non-technical business owners to understand workflows.
- Maintain current knowledge of evolving LLM capabilities, implementation patterns, and AI development stacks.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
