Manager - Data Analytics and AI Engineering

Manager – Data Analytics and AI Engineering

Job Title:

Manager – Data Analytics and AI Engineering

Location:

United States

Department:
Internal Audit Department

Reports to:

Associate Director – Data Analytics and Transformation

We are seeking a highly motivated Data Analytics & AI Engineering Manager to join our Internal Audit department. This role will play a critical part in modernizing and streamlining our audit processes through data and ML pipelines, production deployment of machine learning models, and development of LLM-based retrieval and multi-agent solutions.

• Design and implement scalable analytics and ML pipelines on Databricks for audit use cases (e.g., journal entry analysis, control testing, PBCs).
• Develop, validate and product ionize ML models for anomaly detection, classification, risk scoring, and predictive analytics; ensure reproducibility, versioning and monitoring.
• Architect and deliver secure RAG (retrieval-augmented generation) solutions and vector-based retrieval layers for sensitive audit documents.
• Design and implement LLM-driven applications and multi-agent workflows to automate triage, evidence aggregation, summarization and related audit tasks.
• Define and maintain data product contracts; collaborate with data platform and integration teams to ensure datasets are fit for model training and serving.
• Implement model governance controls including validation, bias detection, explainability, drift monitoring and performance tracking.
• Produce operational dashboards and visualizations (Power BI / Tableau) that translate model outputs into actionable audit information.

Document designs, deliver technical handoffs and conduct knowledge transfers with audit stakeholders while ensuring traceability and auditability of automated solutions.

• Bachelor’s Degree in Data Analytics, Computer Science, Information Systems, Accounting Information Systems, or a related field.
• Minimum 5 years’ professional experience in data science, ML engineering or applied analytics with production deployments.
• Proficiency in Python (including pandas, numpy) and common ML frameworks (scikit-learn, PyTorch or Tensor Flow).
• Experience with experiment and metadata tracking tools (MLflow or equivalent).
• Demonstrated experience building LLM-based applications (RAG pipelines, embeddings/vector search, prompt engineering).
• Familiarity with agent orchestration concepts or frameworks for coordinating multiple models/tools.
• Strong SQL and data modeling skills; ability to translate audit requirements into analytic designs.
• Experience in internal audit, risk management, or compliance functions.
• Practical experience with Databricks / Spark (notebooks, jobs, ML pipelines).
• Familiarity with audit testing methodologies or audit management software.
• Experience with data visualization tools (Power BI, Tableau, Qlik, etc.).
• Exposure to ETL tools, APIs, or cloud-based data platforms.
• Knowledge of process mining or continuous auditing techniques.

• Effective collaboration and communication skills, with a proven ability to work with interdisciplinary teams.
• Strategic thinker with a proactive approach to fraud risk assessment and process optimization.
• Commitment to upholding data integrity, confidentiality, and ethical standards in all forensic and data-driven activities.

• Up to 25% travel

Amphenol Corporation is an equal opportunity employer and we encourage applications from all qualified candidates.