OT Artificial Intelligence Data Engineer (ACE AI Project)

OT Artificial Intelligence Data Engineer

OT Artificial Intelligence | OT Product Line

Position Overview

We are seeking an OT Artificial Intelligence Data Engineer to design, build and operate trusted data pipelines and data products that enable AI solutions in industrial environments. This role transforms high-volume operational, engineering, inspection and contextual data into secure, reliable and AI-ready datasets for computer vision, predictive analytics, generative AI, agentic systems and physical AI.

This position is part of an industrial AI team and provides hands-on data engineering leadership and delivery support for operational technology (OT) AI use cases. The role develops reusable ingestion, transformation, contextualization and serving patterns that connect industrial data sources with AI development, training, validation and inference workflows across edge, on-premises and cloud environments.

The team is highly technical, creative and cross-functional. The Data Engineer will work with industrial domain experts, data scientists, AI engineers, solution architects, cybersecurity teams and operations personnel to make industrial data discoverable, understandable, governed and fit for AI.

Success means shortening the path from raw OT data to dependable AI outcomes without compromising safety, cybersecurity, data integrity or operational reliability.

High-Level Job Description / Scope

This is a dedicated data engineering role supporting industrial AI initiatives. It covers the acquisition, integration, transformation, contextualization, quality, governance and delivery of industrial data used to develop and operate AI solutions.

The role works across historians, control and automation platforms, engineering systems, sensors, images, video, documents, event streams and enterprise data platforms to provide secure, scalable and reusable AI-ready data products.

Key Responsibilities

1. Design, build, test and operate batch and streaming data pipelines that acquire data from industrial and engineering sources for OT AI use cases.

2. Create reusable ingestion and transformation patterns for time-series data, events, alarms, sensor telemetry, images, video, documents and maintenance or inspection records.

3. Contextualize data using asset hierarchies, equipment identifiers, engineering metadata, spatial relationships, operational states and semantic models.

4. Develop curated, versioned and documented datasets for AI model training, validation, testing, inference and performance monitoring.

5. Implement automated data profiling, validation, reconciliation and quality controls for completeness, accuracy, timeliness, consistency and traceability.

6. Maintain data lineage, provenance, schemas, metadata, retention rules and access controls for industrial AI data products.

7. Engineer secure data movement across OT security zones and approved edge, on-premises and cloud environments in alignment with established architecture and cybersecurity requirements.

8. Optimize pipelines and storage for high-volume, high-velocity and intermittently connected industrial environments while controlling latency, reliability and cost.

9. Build observability for data pipelines and data products, including freshness, failures, schema drift, quality degradation and downstream impact.

10. Support data labeling, annotation, sampling, balancing and synthetic-data workflows for computer vision and other industrial AI models.

11. Partner with data scientists and AI engineers to define features, training datasets, ground truth, evaluation data and production inference interfaces.

12. Produce technical documentation, data contracts, source-to-target mappings, data flow diagrams, runbooks and operational support procedures.

13. Contribute actively within an Agile delivery team and continuously improve shared data engineering standards, components and practices.

Required Qualifications

1. Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Systems or a related field, or equivalent experience.

2. Five or more years of experience building and supporting production data pipelines, data platforms or data products.

3. Strong programming skills in Python and SQL, including testing, debugging, performance optimization and maintainable software engineering practices.

4. Experience with batch and streaming data integration, APIs, message brokers, event processing and structured and unstructured data.

5. Experience implementing data quality, schema management, lineage, metadata, observability and automated pipeline monitoring.

6. Experience preparing, curating and versioning training, validation and inference datasets for machine learning or AI workloads.

7. Working knowledge of industrial data sources such as process historians, automation systems, sensors, alarms, engineering data, maintenance data, images or video.

8. Understanding of OT cybersecurity, network segmentation, least-privilege access and secure data movement across industrial environments.

9. Experience with cloud and hybrid data services, containerized workloads, source control, CI/CD and infrastructure or configuration automation.

10. Ability to translate data needs among operations personnel, OT engineers, data scientists, AI engineers, architects and cybersecurity reviewers.

11. Demonstrated ownership of reliable production data services, including troubleshooting, incident response, documentation and continuous improvement.

Preferred Qualifications

1. Master's degree in Computer Science, Data Engineering, Artificial Intelligence or a related field.

2. Experience with Microsoft Azure data and AI services, Microsoft Fabric, Azure Data Lake Storage, Azure Databricks or equivalent platforms.

3. Experience with industrial connectivity and data technologies such as OPC UA, MQTT, historians, edge gateways or industrial IoT platforms.

4. Experience with time-series, lakehouse, graph, vector or geospatial data stores and selecting the right storage pattern for an AI use case.

5. Experience with semantic models, knowledge graphs, digital twins, asset hierarchies or industrial data contextualization.

6. Experience engineering image and video datasets for computer vision, including annotation, augmentation, quality review and large-file handling.

7. Experience with feature stores, vector databases, retrieval-augmented generation data pipelines or multimodal AI data preparation.

8. Experience deploying data processing at the edge in constrained, intermittently connected or disconnected environments.

9. Familiarity with MLOps, model monitoring and the relationship between data drift, model performance and retraining.

10. Experience in industrial environments, including energy, manufacturing or other asset-intensive industries, and comfort working directly with asset and operations personnel.

Critical Selection Criteria

1. Technical Capability

Demonstrated ability to engineer reliable, observable and secure industrial data pipelines and AI-ready data products across edge, on-premises and cloud environments.

2. Data Quality and Context

Ability to assess source-data limitations, establish trustworthy quality controls, and contextualize data so AI teams and operations personnel can use it correctly.

3. OT and AI Understanding

Knowledge of industrial operations, OT constraints and the data lifecycle required to develop, deploy and monitor dependable AI solutions.

4. Collaboration and Communication

Ability to translate operational and AI requirements into clear data contracts, technical designs, delivery priorities and supportable solutions.