AI Solution Specialist II

Job Summary

extracted from CTO Architecture excel

Leads AI solution shaping for strategic pursuits in collaboration with senior engineers, architects, and practice leaders. Defines differentiated solution positioning, value

Key Responsibilities

Lead strategic AI solution shaping with senior architects/engineers; define differentiated positioning and value levers.

Skill Requirements

Other Requirements

Provided by DUH Jalaj.

Role Summary

Lead customer-facing presales solutioning for Physical AI opportunities across manufacturing and asset-intensive industries. Translate operational challenges into differentiated Physical AI propositions, architectures, demos, pilots, proposals, estimates and business cases that are secure, scalable and commercially viable.

Key Responsibilities

• Lead discovery, qualification, solution architecture, proposal development, technical validation and deal closure.
• Conduct workshops to identify business priorities, plant challenges, data readiness, KPIs, constraints and decision criteria.
• Design end-to-end architectures across PLC/SCADA/MES/historians, edge compute, data platforms, cloud, AI/ML and enterprise applications.
• Create customer presentations, solution diagrams, demos, proof-of-concepts and RFI/RFP/RFQ responses.
• Own assumptions, sizing, effort estimates, bill of materials, licensing inputs, dependencies, risks and delivery handover.
• Build business cases linked to uptime, OEE, MTTR, productivity, quality, energy and AI deployment velocity.
• Coordinate sales, product, OT, data/AI, cloud, cybersecurity, delivery, finance and partner teams.
• Create reusable reference architectures, demo assets, discovery questionnaires and proposal components.

Required Experience

• 10-15 years of relevant experience, including at least 4 years in presales, solution architecture, consulting or customer-facing technical leadership.
• Experience owning complex pursuits from discovery and architecture through estimation, proposal, presentation and technical closure.
• Industry exposure in manufacturing, automotive, process industries, energy, utilities, oil and gas, logistics or other asset-intensive sectors.
• Experience shaping pilots and production-scale solutions across plants, lines, assets or geographies.
• Ability to engage confidently with CIO/CDO teams, manufacturing IT/OT leaders, plant stakeholders, architects and procurement.

Essential Skills

• Industrial systems and architecture: ISA-95/ISA-88, PLC, SCADA, MES, historians, CMMS, ERP and industrial data flows.
• Connectivity: OPC UA, MQTT, Modbus, REST/SQL, edge gateways and protocol conversion.
• Edge and cloud: containers, Kubernetes, CPU/GPU edge environments, observability, remote management and hybrid deployment.
• Data and AI: time-series data, event streaming, lakehouse patterns, metadata, lineage, data quality, semantic models, knowledge graphs, MLOps, RAG, vector databases and AI agents.
• Cloud depth in at least one of Microsoft Azure, AWS or Google Cloud.
• Security fundamentals across identity, segmentation, encryption, API security, auditability, data residency and OT cybersecurity.
• Strong discovery, whiteboarding, demo storytelling, proposal writing, estimation, commercial awareness and executive communication.