AI Engineer

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, or related field.
• 4-8 years' experience in data engineering, machine learning, or automation.
• Strong proficiency in Python, SQL, and Power Platform (Power Automate, Copilot Studio, Power Apps).
• Hands-on experience with EDA, regression/classification models, clustering, anomaly detection, and time-series forecasting.
• Familiarity with Azure AI / Azure Machine Learning / Fabric ML environment.
• Understanding of RAG pipelines, prompt-engineering, and LLM integration a plus.
• Experience working with structured (ERP, Finance, HR) and semi-structured data (documents, logs).
• Strong communication skills - can explain technical solutions to non-technical stakeholders.
• Bonus: Exposure to Finance or Shared-Service operations preferred.

Additional Information

Job Description & Requirements Design, build, and deploy AI agents for operational workflows. - Integrate agents with enterprise systems (ERP, HRIS, SharePoint, Dynamics 365, Salesforce) via APIs, Power Automate, or Graph connectors. - Implement agent telemetry and observability pipelines using Microsoft Fabric or Power BI. - Apply RAG (Retrieval-Augmented Generation), context injection, and prompt-chaining for knowledge-aware automation if needed. - Maintain security, versioning, and audit trails for all agent deployments. - Perform exploratory data analysis (EDA) on Finance, HR, or current datasets to identify automation opportunities and trends. - Develop predictive ML models (e.g. anomaly detection, risk scoring). - Deploy ML models into Power BI or agents through Fabric ML endpoints, or Python scripts. - Collaborate with BI team to transform model output into decision-ready dashboards and alerts. - Continuously measure ROI and performance through agent telemetry and statistical evaluation. - Partner with the AI BA to translate business logic into model features and automation workflows. - Work with BI teams to ensure data readiness, integrity, and lineage. - Document models, pipelines, and prompts for auditability and reproducibility.

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