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# Senior AI Engineer

- **Company:** Apperture Solutions
- **Location:** Charlotte, NC, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Unity 3d, Agile Methodology, Artificial Intelligence, Automation of Tests, Microsoft Azure, Code Review, Continuous Integration, Custom Software, Data as a Services, Information Engineering, Digital Architecture, Graph Database, Supervisory Control and Data Acquisition (SCADA), Python (Programming Language), Machine Learning, Message Queuing Telemetry Transport (MQTT), Open Source Technology, Rapid Prototyping Process, Power BI, Kusto Query Language, OPC Unified Architecture, Azure Data Lake, Search Technologies, Microsoft SharePoint, Software Engineering, Data Streaming, Azure Service Bus, Data Logging, Azure Data Factory, Pytorch, Large Language Models, Multi-Agent Systems, Prompt Engineering, Software Security, Git, Fastapi, Containerization, Data Lakes, Information Technology, Api Design, Software Version Control, Databricks
- **Published:** September 11, 2026
- **Apply:** https://www.beyondcharlotte.com/job.asp?id=3386441689&tx=FK3936FFF&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Strong Python software engineering skills and experience building APIs, data services, and AI/ML applications using frameworks such as FastAPI, PyTorch, LangChain, or LangGraph.
 * Hands-on experience building or integrating LLM-based systems using commercial or open-source models and applying prompt engineering, structured outputs, tool calling, and model orchestration.
 * Practical knowledge of RAG architecture, embedding models, vector databases, semantic search, and techniques for evaluating retrieval and generation quality.
 * Demonstrated ability to assess AI-assisted outputs for correctness, security, maintainability, performance, and fitness for use rather than relying on generated results at face value.
 * Experience designing reliable production systems with automated testing, version control, CI/CD, logging, monitoring, containerization, and secure deployment across Azure, edge, or hybrid environments.
 * Ability to understand process, manufacturing, or other operational workflows and convert them into clear data, software, and AI requirements.
 * Sound technical judgment, curiosity, and resourcefulness when working through open-ended problems, incomplete information, and competing design options; ability to explain tradeoffs and risks to engineering and operational audiences.
 * Ability to work independently, collaborate across disciplines, manage priorities, and maintain attention to detail while supporting customer outcomes, responsible AI practices, and continuous learning.
 * Required Education & Experience
 * Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent relevant experience.
 * Five or more years of professional experience in AI/ML, software engineering, data science, or a closely related discipline, including ownership of production solutions.
 * Experience taking at least one AI or ML capability from problem definition through deployment, validation, monitoring, and operational support.
 * Experience working within software delivery practices that include Git, code review, automated testing, and agile or flow-based execution.

Preferred Experience/Competencies

 * Experience with industrial data and controls, including OPC UA, MQTT/Sparkplug B, SCADA/DCS, PLCs, industrial historians, Unified Namespace patterns, or ISA-95/ISA-88 contextualization.
 * Experience with Azure data services such as Event Hubs, Azure Data Explorer/KQL, Databricks, Delta Lake, Unity Catalog, Structured Streaming, or ADLS Gen2.
 * Experience designing schema-versioned data contracts, validation pipelines, time-series data products, or lakehouse medallion architectures.
 * Familiarity with Model Context Protocol (MCP), governed agent tool design, multi-agent orchestration, or human-in-the-loop workflows.
 * Domain exposure in power generation, life sciences, batch manufacturing, or another regulated process industry, including reliability, maintenance, optimization, or operational analytics use cases.
 * Experience with architecture decision records, technical standards, and cross-team contract-based delivery.

Physical Requirements

 * Standing
 * Walking
 * Sitting
 * Kneeling
 * Reaching Overhead
 * Climbing
 * Pushing and Pulling
 * Lifting - 50 pounds
 * Using a Computer
 * Using a Telephone
 * Driving

## Description

* Lead the design and delivery of production-grade AI applications, including LLM agents, RAG systems, workflow co-pilots, and predictive or optimization services.
 * Translate manufacturing workflows, operating constraints, and user needs into well-scoped technical solutions in partnership with operations, engineering, OT, IT, cybersecurity, and business stakeholders.
 * Design agent architectures using frameworks such as LangChain or LangGraph, including supervisor-worker patterns, REST or Model Context Protocol (MCP) interfaces, governed tools, and appropriate human approval points.
 * Develop enterprise retrieval solutions using embeddings, vector stores, semantic search, knowledge graphs, and structured operational content such as SOPs, logs, maintenance records, and engineering documentation.
 * Use contemporary AI-assisted development tools to accelerate delivery, supported by evaluation datasets, test harnesses, guardrails, monitoring, and independent review of model behavior, generated code, and agent recommendations.
 * Design agent-ready data products that route requests appropriately across live edge data, hot time-series stores, and historical lakehouse data.
 * Integrate AI solutions with industrial and enterprise platforms, including OPC UA, MQTT/Sparkplug B, Unified Namespace architectures, SCADA/DCS data, Power BI, SharePoint, and custom applications.
 * Partner with data engineering teams on schema-versioned data contracts, contextualization, validation, and streaming integrations across Azure Event Hubs, Azure Data Explorer, Databricks/Delta Lake, and ADLS Gen2.
 * Package and operate solutions as secure, containerized services for cloud, edge, on-premises, or regulated deployment environments; contribute to CI/CD, observability, and operational support.
 * Frame ambiguous industrial problems, evaluate alternative approaches, and advance promising concepts from rapid prototype to maintainable production capability.
 * Document architecture, design decisions, limitations, validation evidence, data contracts, workflows, and compliance touchpoints; provide technical leadership through reviews, mentoring, and cross-team handoffs.


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