Description

Position Overview

The AI/ML Engineer is a hands-on technical specialist responsible for designing, building, and operationalizing AI and machine learning capabilities within the MDM platform and its surrounding ecosystem. This role focuses on applying AI/ML to core MDM problems — entity matching, deduplication, data quality scoring, anomaly detection, and intelligent automation of data stewardship workflows.

This is not a research role — the AI/ML Engineer builds production-grade models and integrates them into the MDM platform's operational pipelines. The role operates under the "you build it, you support it" model, owning AI/ML features from experimentation through production deployment and ongoing monitoring.

The AI/ML Engineer works closely with the MDM Architect (who drives overall technical direction), the Engineering Manager (who drives delivery), and MDM/Integration developers (who build the platform and pipelines that AI/ML models plug into).

Key Responsibilities AI/ML Model Development & Integration

- Design, build, and train ML models for entity matching, deduplication, and record linkage across customer, supplier, contact, and item domains
- Develop probabilistic and deterministic matching algorithms that improve match accuracy over traditional rule-based approaches
- Build data quality scoring models that assess completeness, accuracy, consistency, and timeliness of master data records
- Develop anomaly detection models to identify data quality issues, unusual patterns, and potential duplicates in real-time data flows
- Design and implement intelligent survivorship logic using ML to determine optimal golden record attribute values from multiple sources
- Build NLP/text processing capabilities for entity name standardization, address parsing, and fuzzy matching
- Integrate AI/ML models into MDM platform workflows — matching, merging, stewardship routing, and exception handling
- Develop automated data classification and categorization models for incoming records
- Build recommendation engines for data stewards — suggest merge candidates, flag potential false positives, prioritize review queues
- Experiment with graph-based approaches for relationship discovery and network analysis across MDM entities

MLOps & Production Operations

- Deploy ML models to production environments with proper versioning, monitoring, and rollback capabilities
- Build and maintain ML pipelines for model training, validation, and deployment (e.g., MLflow, Kubeflow, SageMaker, Azure ML)
- Implement model monitoring — track prediction accuracy, data drift, concept drift, and model degradation over time
- Design A/B testing frameworks to compare model performance against rule-based baselines
- Build automated retraining pipelines triggered by performance degradation or data distribution changes
- Manage feature stores and feature engineering pipelines for MDM-specific features
- Optimize model inference performance for real-time matching scenarios (latency, throughput)
- Maintain model documentation including training data, hyperparameters, performance metrics, and decision thresholds

AI Platform & Agentic Workflows

- Design and build agentic AI workflows integrated with MDM processes (e.g., using AWS Bedrock, LangChain, or similar frameworks)




- Develop LLM-powered capabilities for data enrichment, entity extraction, and intelligent data validation
- Build AI-assisted stewardship tools that reduce manual review effort through intelligent automation
- Implement RAG (Retrieval-Augmented Generation) patterns for contextual data quality recommendations
- Evaluate and integrate foundation models and LLMs for MDM-specific use cases
- Design prompt engineering strategies and guardrails for production LLM integrations
- Build conversational interfaces for data stewards to query and interact with MDM data using natural language

Data Engineering for AI/ML

- Design and build feature engineering pipelines that extract ML-ready features from MDM, ERP, CRM, and data lake sources
- Build training data pipelines — extract, label, and version training datasets from production MDM data
- Implement data preprocessing, cleansing, and normalization pipelines specific to ML model inputs
- Collaborate with data lake and integration teams to ensure AI/ML pipelines have access to required data sources
- Design and maintain data schemas for ML feature stores and model input/output contracts

Production Support & Troubleshooting

- Own production support for AI/ML features — monitor model performance, investigate prediction failures, and resolve issues within SLAs
- Debug model prediction errors — trace through feature extraction, model inference, and post-processing to isolate root cause
- Analyze model logs and prediction outputs to identify systematic errors or bias
- Collaborate with MDM developers when AI/ML model outputs cause downstream data quality issues
- Perform root cause analysis when match/merge accuracy degrades and implement corrective actions
- Maintain runbooks for AI/ML model operations, retraining procedures, and incident response

Testing & Quality

- Design and execute model evaluation frameworks — precision, recall, F1, AUC for matching models
- Build automated test suites for model validation including edge cases, boundary conditions, and adversarial inputs
- Conduct A/B testing and champion/challenger experiments to validate model improvements
- Perform bias and fairness testing across different data segments and domains
- Participate in code reviews for ML code and provide feedback on data pipeline quality
- Maintain regression test datasets to ensure model updates don't degrade performance on known scenarios

Security & Compliance

- Working knowledge of data security, information security practices, and SOX compliance for AI/ML model changes in production
- Ensure PII/sensitive data handling compliance in model training and inference pipelines
- Implement model explainability and audit trails for compliance-sensitive matching decisions

Required Qualifications AI/ML Expertise





- 10 - 12 years hands-on experience building and deploying ML models to production (not just research/experimentation)
- 4+ years experience with entity matching, record linkage, or deduplication using ML approaches (e.g., probabilistic matching, deep learning for entity resolution)
- 4+ years experience with Python ML ecosystem — scikit-learn, pandas, NumPy, TensorFlow or PyTorch
- 3+ years experience with NLP/text processing for entity name matching, address parsing, fuzzy matching (e.g., spaCy, NLTK, Hugging Face Transformers)
- 3+ years experience with MLOps tools and practices — model versioning, deployment, monitoring (e.g., MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI)
- 2+ years experience with LLMs and generative AI — prompt engineering, RAG patterns, agentic workflows (e.g., AWS Bedrock, OpenAI API, LangChain, Claude)
- Experience with graph-based ML or network analysis techniques (e.g., Neo4j, GraphSAGE, node2vec)
- Experience with feature engineering and feature store management (e.g., Feast, Tecton, SageMaker Feature Store)

Data Engineering & Platform Skills

- 5+ years advanced SQL experience — complex queries, performance tuning, data analysis (e.g., Oracle, SQL Server, PostgreSQL, Snowflake)
- 4+ years hands-on coding in Python — production-quality code, not just notebooks (including testing, error handling, logging)
- 3+ years experience building data pipelines for ML — feature extraction, training data preparation, model serving (e.g., Apache Spark, Airflow, dbt)
- 2+ years experience with cloud ML platforms and services (e.g., AWS SageMaker, Azure ML, GCP Vertex AI)
- 2+ years experience with containerization for model deployment (e.g., Docker, Kubernetes)
- Experience with CI/CD for ML models — automated testing, deployment, and rollback (e.g., Jenkins, GitLab CI, GitHub Actions)
- Experience with log analysis and monitoring tools for model observability (e.g., Splunk, ELK, CloudWatch, Prometheus/Grafana)

MDM & Data Quality Domain Knowledge

- 3+ years experience working with MDM platforms or data quality systems (e.g., Informatica MDM, Reltio, Informatica DQ, Ataccama)
- Understanding of MDM concepts: match/merge, survivorship, golden record, hierarchy management, stewardship workflows
- Experience with data quality dimensions — completeness, accuracy, consistency, timeliness, uniqueness
- Experience with incident management using ticketing systems (e.g., ServiceNow, Jira)
- Working knowledge of SDLC: development, testing, CI/CD, change management, release management

Preferred Qualifications

- Manufacturing industry experience — customer, supplier, item master data
- Experience applying ML to multi-domain MDM (customer, supplier, contact, item)
- Experience with Informatica MDM or Reltio platform internals and extensibility
- Experience with graph databases for relationship modeling (e.g., Neo4j, Amazon Neptune)
- Experience with real-time ML inference at scale (low-latency model serving)
- Experience with data labeling and annotation workflows for training data creation
- Experience with model explainability frameworks (e.g., SHAP, LIME)
- Publications or patents in entity resolution, record linkage, or data quality
- Familiarity with event-driven architecture and streaming ML (e.g., Kafka + ML inference)
- Agile/Scrum delivery experience

📌 Application Development & Support Specialist (Pune)
🏢 Vertiv Group
📍 Pune