Area(s) of responsibility

AI Architect

Role Summary

We are looking for an experienced AI Architect to lead the design and delivery of AI/ML solutions applied to enterprise data management and migration workstreams. The candidate will be responsible for architecting intelligent automation across data profiling, mapping, cleansing, validation, enrichment, and deduplication — ensuring high data quality and accelerated delivery timelines.

Key Responsibilities

• Architect end-to-end AI/ML solutions for data profiling, semantic mapping, cleansing, validation, enrichment, and deduplication across enterprise data sets.
• Design source-to-target data mapping strategies leveraging machine learning for confidence scoring, anomaly detection, and pattern recognition.
• Define governance frameworks including human-in-the-loop review, approval workflows, and auditability for all AI-generated recommendations.
• Lead technical design of data transformation and loading strategies for target ERP systems, including format generation (IDOCs, BAPIs, flat files, APIs).
• Establish data reconciliation and validation frameworks — variance analysis, exception management, and post-load quality assurance.
• Drive the creation of reusable assets — mapping repositories, rule libraries, exception patterns — to improve efficiency across iterative execution cycles.
• Serve as the technical lead for the engagement; conduct solution walkthroughs, reviews, and governance discussions with client stakeholders.
• Mentor and guide AI Engineers, Data Engineers, and functional consultants throughout the engagement lifecycle.

Required Skills & Qualifications

• 10+ years in data management, data engineering, or data migration with at least 3+ years applying AI/ML techniques to data quality or migration challenges.
• Strong hands-on experience with AI/ML frameworks — LLMs, NLP, fuzzy matching, entity resolution, anomaly detection (Python, scikit-learn, TensorFlow, PyTorch, Hugging Face, or similar).
• Deep understanding of ERP data models and data loading mechanisms across platforms such as SAP, Oracle, Infor, or JD Edwards.
• Proven experience deploying solutions on cloud platforms — AWS, Azure, or GCP (compute, storage, ML services, orchestration).
• Expertise in data quality disciplines — profiling, standardization, deduplication, enrichment, validation, and reconciliation.
• Experience with iterative migration execution models (Mock, SIT, UAT, Production) and factory-based delivery approaches.
• Familiarity with industry classification and reference data standards (UNSPSC, D&B, NAICS, ICD, HL7, GS1).
• Excellent communication and stakeholder management skills; ability to present technical solutions to business audiences.

Preferred

• Experience in Healthcare, MedTech, Manufacturing, or Life Sciences domains.
• Exposure to Databricks, Apache Spark, or distributed data processing frameworks.
• Certifications in AI/ML (AWS ML Specialty, Azure AI Engineer, Google Professional ML Engineer) or relevant cloud certifications.

Education

• Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, Information Systems, or a related field.