Senior AI Engineer

General Description

The Senior AI Engineer will design, build, and deploy secure, domain-specific AI solutions for industrial operations, including agentic applications, retrieval-augmented generation (RAG), machine learning services, and workflow co-pilots. This role translates complex manufacturing and engineering requirements into practical, supportable products that connect operational data with modern AI capabilities across edge, cloud, and hybrid environments.

Specific Responsibilities

• 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.

Required Competencies

• 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