IQVIAOff Campus DrivesOfficial Job Posting

Software Development Engineer 2 at IQVIA

Kochi, Kerala, IndiaOnsiteFreshers / 0–1 YearsNot specified

IQVIA is hiring a Software Development Engineer 2 to join its analytics team in Kochi, India. In this role, you will assist in designing and building full-stack software solutions for healthcare intelligence and commercial insights platforms. You will work with modern technologies, including Python and Generative AI, to troubleshoot code, build production-ready applications, and drive impact in global health solutions.

Apply Now

IQVIA Software Development Engineer 2 career opportunity

Official Role Details

Role Overview

You will work with modern technologies, including Python and Generative AI, to troubleshoot code, build production-ready applications, and drive impact in global health solutions.

Applicant Requirements

Eligibility Criteria & Qualifications

Degree / QualificationB.E / B.Tech / MCA / B.Sc

Eligible BatchesNot specified

Official Eligibility Points

Bachelor's Degree in Computer Science, Engineering, or equivalent practical experience.
Hands-on experience in object-oriented programming with Python and Generative AI.
Proven capability to develop production-ready applications using Python and Gen AI.
Solid understanding of relational databases and proficiency in basic SQL queries.

Day-to-Day Deliverables

Key Responsibilities

Model Development & Experimentation

•Assist with the design and development of software solutions across both front-end and back-end application layers.
•Support small software development projects based on specific guidelines and discretion.

Data Pipelines & Feature Engineering

•Troubleshoot code issues and continuously improve IQVIA's platforms to fulfill user requirements.

Evaluation, Deployment & Optimization

•Participate in the review of functional specifications and software validation deliverables.

Technical Qualifications

Required Skills & Technologies

PythonGenerative AISQLRelational DatabasesFull Stack DevelopmentSoftware ArchitectureProblem Solving

Official Application Method

How to Apply

Submit your application directly through IQVIA's verified careers portal.

Review the published role requirements, degree qualifications, and eligible graduation batches above.

Ensure your resume highlights relevant projects and contains working GitHub repository links.

Click the official application button below to open IQVIA's direct careers page.

Complete and submit your application directly on the employer's portal.

Apply on IQVIA Official Careers Page

You will be directed to IQVIA's official careers portal. Career Insights does not process applications or charge any candidate fees.

The specifications above are verified official details published by IQVIA. Candidates should verify all final terms, deadlines, and eligibility directly on IQVIA's official careers portal before submitting an application.

Career Insights

Recommended Preparation

Independent editorial guidance based on the published role information.

What to Prepare

Evaluate your machine learning background against published criteria and modeling preparation focus areas.

Education & Academic Criteria

Cohort: Eligible: Not specifiedPublished Requirement

Published Requirement

B.E / B.Tech / MCA / B.Sc

Preparation Focus

Hiring evaluations prioritize demonstrable problem-solving, domain competence, and practical project evidence over specific degree pedigree.

Machine Learning & Python

Published Requirement

Applied machine learning, statistical modeling, and Python libraries.

Preparation Focus

Revise ML evaluation metrics (Precision, Recall, F1, ROC-AUC), feature engineering, loss functions, and model deployment via REST endpoints (FastAPI).

Data Pipelines & Model Training

Published Requirement

Model development, dataset preprocessing, and experimentation.

Preparation Focus

Understand dataset splits, regularization techniques to prevent overfitting, vector embeddings, and modular code hygiene in ML pipelines.

Data Storage & Persistence

Published Requirement

Familiarity with databases, schema modeling, and structured query writing.

Preparation Focus

Be prepared to explain relational normalization (1NF-3NF), indexing trade-offs, foreign key constraints, and transactional consistency.

Version Control & Engineering Hygiene

Published Requirement

Familiarity with Git branching, pull requests, and collaborative repository management.

Preparation Focus

Review Git branch workflows, pull request code reviews, and maintain clean commit history with descriptive messages.

Work Mode & Collaboration

Published Requirement

Onsite (Kochi, Kerala, India) operational structure.

Preparation Focus

Highlight proactive written communication, technical documentation discipline, and the ability to troubleshoot errors systematically before escalating.

Technical Topics

Depending on the hiring team’s evaluation style, technical assessments for this role typically cover these core areas:

Topic 1: Python & Machine Learning Fundamentals

Recommended Preparation

Python data science libraries (NumPy, Pandas, PyTorch / TensorFlow)
Supervised and unsupervised ML algorithms, loss functions, and optimizers (SGD, Adam)
Feature normalization, embeddings, train/validation split discipline, and preventing data leakage

Preparation Tip: Be ready to explain how you select appropriate baseline algorithms and write modular, testable Python code without data leakage.

Topic 2: NLP, LLM APIs & Transformer Concepts

Recommended Preparation

Tokenization, embeddings, attention mechanisms, and Transformer architectures
Prompt engineering, structured output extraction, and LLM API integration
Retrieval-Augmented Generation (RAG) and semantic search workflows

Preparation Tip: Demonstrate understanding of tokenization nuances, context window limits, and how LLM outputs can be validated against structured schemas.

Topic 3: Document Understanding & Computer Vision

Recommended Preparation

Document layout analysis, optical character recognition (OCR) pipelines, and bounding-box validation
Table extraction, key-value pair parsing, and multimodal vision-language concepts
Handling image noise, scan distortions, and irregular document structures

Preparation Tip: Be prepared to explain document parsing workflows: differentiating layout detection, text OCR, and relationship extraction on complex documents.

Topic 4: Evaluation Methodology, Metrics & Error Diagnostics

Recommended Preparation

Precision, Recall, F1-Score, ROUGE, BLEU, and exact match metrics
Curating rigorous test sets and designing stratified validation splits
Error analysis: categorizing model failure modes, confusion matrices, and verifiable AI

Preparation Tip: Emphasize evaluation discipline: why aggregate accuracy is insufficient and how you curate challenge sets to diagnose specific model vulnerabilities.

Topic 5: Experimentation, Research Documentation & Reproducibility

Recommended Preparation

Systematic experiment tracking (MLflow, Weights & Biases) and ablation study design
Documenting research hypotheses, benchmarks, and reproducible pipelines
Communicating model confidence thresholds, error taxonomy, and verifiable AI considerations

Preparation Tip: Highlight scientific rigor: explain how you structure experiments so that any team member can reproduce your training runs and benchmark results.

* Note: These are independent Career Insights editorial recommendations to help candidates structure their study plan. They do not represent guaranteed interview stages or official hiring mandates from IQVIA.

Practical Project

Demonstrate the machine-learning workflow by building a small project that covers data preparation, modeling, evaluation, and reproducible results.

Practical Project

Document Intelligence & Agentic Table Extraction Pipeline

AI/ML Research Project

What to Build

A reproducible document intelligence pipeline in Python that extracts structured tabular data from complex, multi-page PDF documents or scanned invoices using layout-aware models or LLM-assisted verification, complete with automated benchmark scoring.

Skills Demonstrated

Python, PyTorch / Transformers, Document AI & OCR, Model Evaluation & Error Diagnostics, Test-Set Curation, Reproducible Research Documentation.

GitHub Deliverables

Clean repository with environment setup (requirements.txt), sample evaluation dataset, benchmark scripts with precision/recall scoring, error analysis notebooks, and clear architecture documentation.

Interview Talking Points

How you designed your evaluation test set, failure mode taxonomy (how the model handles rotated scans or dense tables), and the trade-offs between heuristic rules vs deep learning models.

Career Insights recommendation: This project is an independent editorial recommendation designed to help candidates build verifiable portfolio evidence. It is not required or sponsored by IQVIA.

Before You Apply

Verify your model notebooks, evaluation summaries, and application materials before applying.

1. Role-Tailored Resume & GitHub Links

Ensure your resume clearly highlights hands-on projects involving Python, Generative AI, SQL. Include verified, publicly accessible GitHub repository links with clean README files.

2. Eligibility & Batch Verification

Confirm that your graduation year (Not specified), educational background, and location match the employer's published requirements before applying.

3. Standout Project Code & Architecture Review

Have 1–2 key projects prepared for technical discussion. Be ready to explain your database schema choices, API contracts, error boundaries, and design trade-offs.

4. Direct Submission on IQVIA's Official Portal

Submit your application directly through IQVIA's official careers portal using the verified apply link below. Career Insights does not process applications or charge any candidate fees.

Career Growth Context

Career Insights editorial perspective on typical milestones and research contribution cycles in applied ML internships.

Research Orientation & Baseline Benchmarking

Familiarizing oneself with the team’s research tools, dataset pipelines, and core model baselines under technical guidance.

Hands-on Experimentation & Error Diagnostics

Conducting exploratory modeling experiments, benchmarking against baselines, and performing rigorous error analysis on complex inputs.

Validation, Reproducibility & Research Documentation

Documenting experimental methodology, optimizing inference trade-offs, and validating agentic or document intelligence workflows.

Performance-Based Conversion Opportunity

Demonstrating consistent research rigor, problem-solving, and technical ownership. As noted in available employer postings, full-time conversion represents an opportunity evaluated based on individual performance and organizational needs rather than a guaranteed progression.

Career Insights Context: Career Insights Editorial Perspective: Development pathways described here reflect general industry research patterns. Specific team workflows, mentorship structures, and conversion evaluations vary by organization. Infrrd’s published posting noted an opportunity for full-time conversion based on performance.

Career Insights Editorial Standards

This page provides verified job details sourced from IQVIA's recruitment announcement, alongside independent preparation roadmaps authored by the Career Insights editorial team. Career Insights is not affiliated with IQVIA and does not collect candidate data or charge application fees.