Experience:

• 3–5 years of experience in AI/ML engineering, applied AI, or data engineering with AI/ML components

Qualification:

• Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, or related field
• Master's degree in Computer Science, Data Science, Artificial Intelligence, or related field

Responsibilities:

• Develop and implement AI/ML models for automated schema discovery, data profiling, and statistical analysis of enterprise data sets
• Build semantic mapping solutions using NLP and LLM techniques generating source-to-target field mappings with confidence scoring
• Implement automated data cleansing and standardization logic including format normalization, unit conversion, naming convention enforcement, and industry-specific rule application
• Develop validation pipelines using AI-inferred rules based on target system metadata including mandatory field checks, referential integrity, and business rule enforcement
• Build enrichment modules to identify and fill missing data attributes using external reference sources and domain knowledge bases
• Implement deduplication solutions using exact and fuzzy matching algorithms; configure survivorship logic and golden record creation
• Create dashboards and reports for data quality metrics, mapping confidence, exception tracking, and load readiness indicators
• Capture learnings and outputs into reusable knowledge repositories such as mapping dictionaries, rule libraries, and exception pattern catalogs

Additional responsibilities:

• Work under the guidance of the AI Architect to build, test, and deploy intelligent automation components

Nice to have:

• Exposure to ERP data loading formats such as IDOCs, BAPIs, flat file imports, API-based loading
• Experience in Healthcare, MedTech, or Manufacturing data domains
• Prior experience building AI/ML models for data quality, entity matching, or data classification use cases

More skills:

AI/ML libraries, Hugging Face Transformers, data quality techniques, profiling, cleansing, deduplication, standardization, enrichment, cloud platforms, ML/AI services, Vertex AI, data services, S3, Glue, Databricks, ETL/ELT concepts, data pipeline design, metadata-driven automation, Python visualization libraries, Matplotlib, Plotly, Streamlit, BI tools, version control, Git