Artificial Intelligence Engineer

Roles & Responsibilities:

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.

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.

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Basic Qualifications:

Bachelors/Masters in CS/Math/Engineering

3 to 5 years experience in Data Science /Applied ML/ ML Engineering with proven leadership delivering

production grade ML system at scale.

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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.

If interested, please share your resume at nikita.tiwari@sattvahuman.com & pooja.chauhan@sattvahuman.com