Bajaj Finserv Off Campus 2026 Hiring Fresher For Product Specialist | Pune

Bajaj Finserv Off Campus Drive 2026 : Bajaj Finserv Off Campus 2026 hiring fresher for Product Specialist Role for Bachelor’s / Master’s Degree graduates and any batch graduates are eligible. The detailed company eligibility and application details are given below.  

About Bajaj Finserv :

Founded in April 2007, Bajaj Finserv is the financial arm of the Bajaj group. We believe in a simple philosophy to never settle for good and go for great. This reflects in our extensive product portfolio that spans across 3 broad categories- lending, insurance and wealth advisory. With 24 products spread across 12 product lines, we’re one of the fastest growing and most diversified NBFCs in India. Our footprint spans the length and breadth of India.

Job Description :

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.

Job Title : Product Specialist
Job Type : Full Time
Location : Pune
Experience : Fresher – 1 Year

Role and Responsibility :

• 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.
• 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.
• 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.
• 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.
• 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.

Education and Skills :

• Bachelor’s/Master’s in CS/Math/Engineering
• 0 – 2 years’ experience 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

How To Apply Bajaj Finserv Off Campus Drive 2026 ??  

All interested and eligible candidates can apply before expire in the following link.  

Apply Link : Click Here

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