Associate Lead

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-scale ML systems