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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Application Engineer, EMEA - **Company:** Seeq - **Location:** United Kingdom (Remote available) - **Salary:** £101,790 - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Microsoft Windows, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Systems Engineering, Unit Testing, Microsoft Azure, Computerized Maintenance Management Systems, Code Review, Encodings, Computer Programming, Continuous Integration, Data Architecture, IBM Maximo, Wonderware, Python (Programming Language), Power BI, Microsoft Copilot, OPC Unified Architecture, Azure Machine Learning, SAS (Software), Microsoft SharePoint, Software Engineering, SQL Databases, Systems Integration, Tableau (Software), Data Logging, Data Processing, Google Cloud, Feature Engineering, GE Proficy, Large Language Models, Snowflake, Multi-Agent Systems, Prompt Engineering, AWS Lambda, Agentic-AI, Git, Pandas, Build Management, Microsoft Fabric, Microsoft Copilot Studio, Containerization, Data Lakes, AI Platforms, Information Technology, Data Analytics, Data Management, Invoking Functions, Software Coding, Restful APIs, Industrial Software, Software Version Control, Databricks - **Published:** November 11, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5960878603 ## About the Role · Agentic AI experience: hands-on experience designing, building, and deploying AI agents or LLM-powered workflows, including prompt engineering, tool/skill design, and handling multi-step reasoning. · Process manufacturing experience in O&G, Chemicals, Pharma, Power, Pulp & Paper, Food & Beverage, Semiconductor, or related industries; ability to translate operational objectives into analytics-driven and agent-driven workflows. · Time series analytics proficiency, including feature engineering, event/condition detection, asset segmentation, anomaly/quality logic, and KPI design. · Historian and OT data experience with platforms such as AVEVA PI, IP.21, DeltaV, Wonderware, Canary, or GE Proficy. · Hands-on Seeq skills, including building solutions in Seeq Workbench, Organizer, Vantage, and Data Lab. · Customer-facing delivery experience leading pilots/POCs to production; strong value articulation, scope management, and cross-functional alignment. · Education: Bachelor's degree (or higher) in Computer Science, Software Engineering, Chemical, Mechanical, Electrical, Industrial, or Systems Engineering, Applied Math, Data Analytics, or related discipline - or equivalent relevant experience. · Experience integrating AI agents with Microsoft Copilot / Copilot Studio, SharePoint knowledge sources, Power Platform, or Microsoft Fabric. · Experience integrating with CMMS systems (e.g., IBM Maximo) and other enterprise systems via APIs to enable closed-loop, anomaly-to-action workflows. · Familiarity with the Process Manufacturing Industry-specific, including Oil & Gas, Pharmaceuticals, Power, Mining, etc. · Familiarity with cloud/data platforms and modern industrial data architectures (Azure/AWS/GCP, Databricks, Snowflake, Historians). · Experience with software engineering practices: Git, code review, unit testing, CI/CD, containerization, observability/logging, and secure handling of credentials. · Strong technical communication across SME, manager, and executive audiences; proven in demos, workshops, and AI solution storytelling. The employee is regularly required to communicate effectively, work with digital tools, and collaborate across time zones in a remote environment; occasional travel to customer sites, partner locations, or events may be required (typically 20-40%, varying by project portfolio). ## Description The AI Applications Engineer leads end-to-end Seeq Intelligence implementations - from use case discovery and solution architecture, through Python-based agent and Skill development, to enablement and lifecycle ownership. Typical solution patterns include anomaly-to-action workflows that integrate with Industrial Software technologies (historian, CMMS, APM, etc.), Microsoft Copilot integrations, AI-generated operational reporting, and customer internal documentation and SME knowledge. Seeq builds advanced analytics and decision intelligence software for industrial process data and operates as a remote-first company with a strong culture of collaboration and iteration. Requirements The essential functions include, but are not limited to, the following: Design and Build Agentic AI Solutions · Author scalable AI agents utilizing that extend and orchestrate Agent Q for repeatable, multi-step industrial workflows (e.g., AI-generated operational reports, batch deviation analysis, root cause investigations, condition-monitor-driven investigations). · Build custom AI agents outside of Seeq to interface applications in the broader industrial technology stack with Agent Q and Seeq's AI platform. · Design and implement Skills and Code Tools in Python (Seeq Data Lab, SPy library) that give agents reliable, governed access to enterprise systems - CMMS, historians, document stores, and customer-built AI services. · Build solution components (accelerators, templates, calculators, KPIs, dashboards, condition monitors, Vantage Rooms) in Seeq Workbench, Organizer, Vantage, and Data Lab that the agent layer can act on and reason about. · Package agentic solutions for reuse with documented prompts/instructions, journals, example datasets, validation plans, and success criteria ready for partner and customer delivery. · Configure Agent Extensibility to wire Seeq Intelligence into customer ecosystems (CMMS, integrity tools, MES, Power BI/Teams, Microsoft Copilot, Snowflake, Databricks). Software Engineering and Architecture · Write production-quality Python for agent Skills, Data Lab notebooks, scheduled/event-triggered workflows; apply modern software engineering practices (version control, code review, testing, CI/CD where applicable). · Author and consume REST/HTTPS APIs for third-party AI agents and enterprise systems; manage authentication patterns (SSO, delegated permissions, SAS URIs) for governed agent access. · Connect solutions to enterprise data ecosystems and downstream tools (e.g., OPC UA/OPC HDA; AVEVA PI/PI AF; SQL databases, data lakes/warehouses; Databricks; Azure ML; AWS Lambda; Power BI/Tableau; Microsoft 365 / SharePoint) following security, scalability, and performance best practices. · Apply prompt engineering, agent instruction design, and evaluation techniques to make agent behavior consistent, trustworthy, and reproducible at industrial scale. Collaborate Cross-Functionally · Partner with the Software Team, Industry Principals, Product Management, and Revenue Teams to align Intelligence solutions to roadmap and close capability gaps. · Support Product Marketing, Solution Consulting, and Sales with credible Intelligence demos, narratives, and GTM assets; assist targeted pre-sales engagements where agentic solution expertise is required. Enable Customers and Partners · Create enablement kits (deployment guides, reference architectures, agent design playbooks, sample Skills/notebooks) and mentor Seeq partners and customer champions to scale agentic delivery globally. · Coach customer subject matter experts on encoding their expert reasoning into agent instructions, journals, and Custom Agent workflows so institutional knowledge becomes shareable and operational. Continuous Improvement · Gather field feedback, monitor agent adoption and quality, and iterate on solution reliability, trustworthiness, and learnability; contribute reusable agent patterns into Seeq's solution library and training content. · Software development proficiency in Python, including writing maintainable code, packaging, working with REST APIs, and using libraries such as Pandas; familiarity with modern AI/LLM tooling concepts (prompts, tool/function calling, retrieval, evaluation, agent orchestration).