AI Engineer

Job Summary :

We are seeking a highly innovative and skilled AI Engineer to join our AI CoE for the Data Integration Project. The ideal candidate will be responsible for designing, developing, and deploying intelligent assets and AI agents that automate and optimize various stages of the data ingestion and integration pipeline. This role requires expertise in machine learning, natural language processing (NLP), knowledge representation, and cloud platform services, with a strong focus on building scalable and accurate AI solutions.

Key Responsibilities :

• LLM-based Auto-schematization : Develop and refine LLM-based models and techniques for automatically inferring schemas from diverse unstructured and semi structured public datasets and mapping them to a standardized vocabulary.
• Entity Resolution & ID Generation AI : Design and implement AI models for highly accurate entity resolution, matching new entities with existing IDs and generating unique, standardized IDs for newly identified entities.
• Automated Data Profiling & Schema Detection : Develop AI / ML accelerators for automated data profiling, pattern detection, and schema detection to understand data structure and quality at scale.
• Anomaly Detection & Smart Imputation : Create AI-powered solutions for identifying outliers, inconsistencies, and corrupt records, and for intelligently filling missing values using machine learning algorithms.
• Multilingual Data Integration AI : Develop AI assets for accurately interpreting, translating (leveraging automated tools with human-in-the-loop validation), and semantically mapping data from diverse linguistic sources, preserving meaning and context.
• Validation Automation & Error Pattern Recognition : Build AI agents to run comprehensive data validation tool checks, identify common error types, suggest fixes, and automate common error corrections.
• Knowledge Graph RAG / RIG Integration : Integrate Retrieval Augmented Generation (RAG) and Retrieval Augmented Indexing (RIG) techniques to enhance querying capabilities and facilitate consistency checks within the Knowledge Graph.
• MLOps Implementation : Implement and maintain MLOps practices for the lifecycle management of AI models, including versioning, deployment, monitoring, and retraining on a relevant AI platform.
• Code Generation & Documentation Automation : Develop AI tools for generating reusable scripts, templates, and comprehensive import documentation to streamline development.
• Continuous Improvement Systems : Design and build learning systems, feedback loops, and error analytics mechanisms to continuously improve the accuracy and efficiency of AI-powered automation over time.

Required Skills and Qualifications :

Preferred Qualifications :

Benefits :