DescriptionForward Deployed Engineer - Operational Intelligence PracticeLocation: Remote - North America
Position SummaryThe Forward Deployed Engineer will build and deploy industrial data and AI solutions for the InSource Solutions Group Operational Intelligence (OI) Practice, a new consulting practice designed to help manufacturers convert digital and AI investments into measurable operational and financial results.
This is a high-impact, high-visibility role for a hands-on, highly self-directed professional who combines industrial data expertise, engineering capabilities, and the judgment to work directly within client operations.
Working closely with the Operational Intelligence Engagement Manager, who leads the definition of client problems and success measures, the Forward Deployed Engineer translates defined business outcomes into working technical solutions. This includes preparing and connecting operational technology (OT) and information technology (IT) data, developing models and analytics using the InSource partner portfolio, and enabling AI workflows that operators and engineers can use in their daily work.
The role contributes technical expertise during discovery and readiness assessments and remains actively involved throughout pilot development, production deployment, and solution expansion.
As a founding member of the OI Practice, the Forward Deployed Engineer will also help establish reusable technical accelerators, delivery standards, and scalable approaches that support the Practice's growth.
Success requires hands-on expertise in industrial data, modern data platforms, and enterprise AI, along with the communication skills to collaborate effectively with operators, engineers, IT teams, and manufacturing leadership.
Key ResponsibilitiesHands-On Solution Delivery- Lead the technical development of proof-of-value pilots, translating defined client outcomes and success measures into working solutions.
- Build, configure, and optimize models and analytics using platforms such as Braincube, AVEVA Advanced Analytics/TwinThread, and AVEVA Predictive Analytics.
- Develop solutions supporting predictive maintenance, process optimization, quality improvement, and other industrial use cases.
- Validate results directly with operators and engineers, refining solutions to establish trust and practical value within manufacturing operations.
- Collaborate with client teams to transition successful pilots into production environments and resolve technical challenges throughout deployment.
OT/IT Data Engineering- Assess and integrate industrial data sources, including SCADA, historians, MES, and CMMS, with enterprise systems such as ERP platforms.
- Develop integration strategies that connect OT and IT data, including time-series and event-based information.
- Evaluate client data readiness, including source availability, connectivity, granularity, historical coverage, and data quality.
- Design, build, and maintain industrial data flows using AVEVA CONNECT based on use-case requirements and long-term client needs.
- Establish asset hierarchies, data contextualization, and standardization to support scalable analytics and AI across manufacturing lines and facilities.
- Extend industrial data pipelines into enterprise platforms such as Databricks and Snowflake when required by client architecture.
Industrial AI and Agentic Workflows- Develop AI assistants and agentic workflows using enterprise large language models such as Claude and ChatGPT.
- Ground AI solutions in contextualized operational data, procedures, maintenance records, and other relevant client information.
- Integrate AI workflows with InSource partner technologies and enterprise data platforms to deliver actionable insights.
- Implement appropriate AI guardrails, human review, evaluation processes, and OT security protections.
- Help clients establish scalable technical foundations for broader AI adoption and long-term operational transformation.
Embedded Client Partnership- Work directly alongside client operations, engineering, and IT/OT teams, including on-site engagement within manufacturing facilities.
- Provide technical expertise during discovery workshops and readiness assessments, including feasibility analysis, identification of data gaps, and effort estimation.
- Explain technical decisions, trade-offs, and recommendations clearly to operational, technical, and executive stakeholders.
- Collaborate closely with the Engagement Manager to align technical execution with defined business outcomes.
- Transfer technical knowledge and documentation to client teams so they can maintain, operate, and extend deployed solutions.
Practice Development and Cross-Functional Collaboration- Develop reusable technical components, including data models, pipeline templates, AI agent patterns, and solution accelerators.
- Document technical designs, configurations, decisions, and implementation details to support effective handoffs.
- Collaborate with InSource technical teams, partner vendors, Delivery, and Managed Services teams on platform configuration, integration, and technical escalations.
- Incorporate lessons learned from client engagements into Practice methodologies, service offerings, and technical standards.
- Contribute to establishing scalable delivery approaches that support the transition from pilot projects to production deployments.
Success Metrics- Deliver working, client-validated pilots on schedule and within agreed scope.
- Successfully transition solutions from proof-of-value into production and expanded deployment.
- Demonstrate measurable operational improvements against client-defined success measures.
- Establish sustainable industrial data foundations that support ongoing analytics and AI use cases.
- Develop reusable technical assets that improve delivery efficiency across engagements.
- Ensure clients and internal teams can operate, maintain, and extend deployed solutions through effective knowledge transfer and documentation.
QualificationsSkills and Competencies- Strong proficiency in Python and SQL for data engineering, analytics, and model development, including version control tools such as Git.
- Advanced understanding of industrial data structures, including time-series and event-based data.
- Ability to design and implement industrial data pipelines using platforms such as AVEVA CONNECT.
- Hands-on capability developing LLM-enabled applications, AI assistants, and agentic workflows, including retrieval, tool integration, and evaluation.
- Ability to leverage AI-powered tools to accelerate coding, analysis, documentation, and technical delivery.
- Strong understanding of OT data environments, including historians, SCADA, MES, CMMS, and industrial connectivity.
- Ability to translate operational and business requirements into practical technical solutions.
- Strong analytical, troubleshooting, and problem-solving capabilities.
- Excellent communication skills, with the ability to explain technical concepts to operators, engineers, IT teams, and executive stakeholders.
- Demonstrated ownership, initiative, accountability, and ability to work independently in client environments.
- Builder mindset with enthusiasm for developing a new consulting practice and creating repeatable technical solutions.
- Collaborative approach when working with Engagement Managers, client stakeholders, internal technical teams, and partner vendors.
- Strong professionalism, integrity, curiosity, and commitment to client outcomes.
- Ability to work effectively within manufacturing facilities and plant-floor environments.
Experience and EducationRequired Experience- Minimum of five years of experience in industrial data engineering, OT/IT integration, manufacturing analytics, data science, solutions engineering, or forward deployed engineering within manufacturing or industrial environments.
- Hands-on experience developing and deploying analytics, machine learning models, or data products, including at least one solution successfully transitioned from prototype into production.
- Working knowledge of industrial OT data sources and structures, including historians, SCADA, MES, CMMS, time-series data, and event data.
- Experience integrating OT and IT data into cloud or enterprise data platforms.
- Hands-on experience developing applications with large language models, including prompt design, retrieval, or tool-enabled workflows.
- Experience working directly with clients, manufacturing facilities, or operations teams in a customer-facing technical role.
- Demonstrated ability to translate business requirements into technical designs and communicate trade-offs to non-technical stakeholders.
Preferred Experience- Experience with the AVEVA ecosystem, including PI System, PI Asset Framework, AVEVA CONNECT, AVEVA Advanced Analytics/TwinThread, AVEVA Predictive Analytics, and AVEVA Asset Performance Management.
- Experience with Braincube or comparable manufacturing analytics platforms.
- Experience with Databricks, Snowflake, Azure, or AWS data services, including lakehouse and streaming architectures.
- Experience establishing asset hierarchies, data contextualization, and standardization across manufacturing lines and facilities.
- Familiarity with industrial connectivity standards and architectures, including OPC UA, MQTT, ISA-95, and unified namespace.
- Experience developing production-ready agentic AI solutions, including multi-step agents, tool integrations such as Model Context Protocol (MCP), and evaluation frameworks.
- Industry experience in mining, building products, consumer packaged goods, and food and beverage manufacturing.
- Previous experience in consulting, systems integration, or forward deployed engineering.
Education- Bachelor's degree in Engineering, Computer Science, Data Science, or a related technical field preferred.
- Advanced degree considered an asset.
Travel Requirements- Ability to travel throughout North America for client engagements, including extended periods working on-site at manufacturing facilities.
- Expected travel requirement: 40-60%.
CompensationCompensation includes a competitive base salary, performance-based incentive plan, and comprehensive benefits. Total earnings potential will be commensurate with experience, qualifications, and demonstrated success delivering industrial data and AI solutions in client environments.
