Associate Lead

Job description

**Job Purpose** As an AI Engineer in Data Intelligence Unit, you will help build and operate the core building blocks of Data Representation learning and Context Engineering across the credit Risk, Fraud/FRM, Sales, Collections Recovery. You will work with senior engineers and Data scientists to converts raw structured and unstructured data into reliable features, embeddings, retrieval-ready knowledge assets, and repeatable evaluation pipelines so downstream AI pods can ship models faster, safer, and with measurable quality. **Duties and Responsibilities** 1) Data Representation Pipelines Prepare and validate datasets from multiple sources (transactions, bureau, device/digital, documents, CRM/operations) Implement features engineering pipelines (aggregations, ratios, behavior signals) and maintain feature definitions. Build large-scale ML systems: distributed training pipelines, feature stores, model registry, CI/CD for ML, and scalable batch + near-real-time scoring services. Support embedding workflows (text/customer/device/dealer/geo) including batch refresh, versioning, and lineage. 2) Knowledge Engineering Support (Canonical Objects Metadata Assets) Help create/maintain canonical objects, entity dictionaries, taxonomies/ontologies, and labeling guidelines. Support annotation/labeling workflows (quality checks, consistency, sampling) for training and evaluation. 3) Experimentation Model Operations Execute training/inference jobs using established frameworks, log experiments and outcomes. Perform error analysis, data leakage checks, and basic model monitoring (drift signals, data anomalies) Contribute to deployment readiness: tests, reproducible configs, and incident triage support. 4) Retrieval Context Engineering Support (LLM/RAG enablement) Assist document processing: chunking, cleaning, metadata tagging, indexing access filters. Maintain prompt/context templates, grounding rules, and evaluation sets for RAG/LLM assistants used by Pods. Run offline evaluations (retrieval quality, answer quality, regressions) and track metrics across releases. 5) Engineering Hygiene Governance Write clean, testable code; follow Git workflows and CI checks. Maintain documentation: dataset cards, feature notes, pipeline SOPs, and release checklists. Follow security/privacy controls for regulated data, ensuring traceability and auditability. **Major Challenges** 1) Data Representation Pipelines Prepare and validate datasets from multiple sources (transactions, bureau, device/digital, documents, CRM/operations) Implement features engineering pipelines (aggregations, ratios, behavior signals) and maintain feature definitions. Build large-scale ML systems: distributed training pipelines, feature stores, model registry, CI/CD for ML, and scalable batch + near-real-time scoring services. Support embedding workflows (text/customer/device/dealer/geo) including batch refresh, versioning, and lineage. 2) Knowledge Engineering Support (Canonical Objects Metadata Assets) Help create/maintain canonical objects, entity dictionaries, taxonomies/ontologies, and labeling guidelines. Support annotation/labeling workflows (quality checks, consistency, sampling) for training and evaluation. 3) Experimentation Model Operations Execute training/inference jobs using established frameworks, log experiments and outcomes. Perform error analysis, data leakage checks, and basic model monitoring (drift signals, data anomalies) Contribute to deployment readiness: tests, reproducible configs, and incident triage support. 4) Retrieval Context Engineering Support (LLM/RAG enablement) Assist document processing: chunking, cleaning, metadata tagging, indexing access filters. Maintain prompt/context templates, grounding rules, and evaluation sets for RAG/LLM assistants used by Pods. Run offline evaluations (retrieval quality, answer quality, regressions) and track metrics across releases. 5) Engineering Hygiene Governance Write clean, testable code; follow Git workflows and CI checks. Maintain documentation: dataset cards, feature notes, pipeline SOPs, and release checklists. Follow security/privacy controls for regulated data, ensuring traceability and auditability. **Required Qualifications and Experience** Basic Qualifications: Bachelors/Masters in CS/Math/Engineering 0 2 yearsexperience in Data Science /Applied ML/ ML Engineering with proven leadership delivering production grade ML system at scale. Required Skills Competencies Core (must-have) Programming: Python (strong), SQL (strong); Git; basic unit testing. Data: Pandas/PySpark basics, joins/aggregations/window functions, data validation and profiling. ML Fundamentals: supervised/unsupervised learning, embeddings, train/val/test discipline, metrics, and error analysis. Applied System Mindset: reproducibility, structured debugging, logging/monitoring fundamentals. Good-to-have Skills ML frameworks: Pytorch / TensorFlow; experiment tracking (MLFlow) Retrieval stack: vector indexing concepts, chunking strategies, hybrid search ideas, evaluation datasets. Data/Infra: Airflow/Prefect, Spark, Elasticsearch/OpenSearch, MongoDB, feature stores, graph Database, vector database, model serving basics. Preferred Qualifications Experience building end-to-end decisioning platforms with real-time and batch orchestration. Graph ML / entity-resolution experience for relationship-based risk and fraud analysis. Experience operating ML systems across multiple products/segments with multi-tenant controls. Publications/patents or strong track record of innovation in applied ML, large-sc