Draup

We are looking for:

• 2+ years of expertise in software development with one or more of the general programming languages (e.g., Python, Java, C/C++, Go). Experience in Python and Django is recommended.

• Deep understanding of how to build an application with optimized RESTful APIs.

• Knowledge of a web framework like Django or similar with ORM or multi-tier, multi-DB-based data-heavy web application development will help your profile stand out.

• Knowledge of Gen AI tools and technologies is a plus.

• Sound knowledge of SQL queries & DB like PostgreSQL(must) or MySQL. Working knowledge of NoSQL DBs (Elasticsearch, Mongo, Redis, etc.) is a plus.

• Knowledge of graph DB like Neo4j or AWS Neptune adds extra credits to your profile.

• Knowing queue-based messaging frameworks like Celery, RQ, Kafka, etc., and distributed system understanding will be advantageous.

• Understands a programming language's limitations to exploit the language behavior to the fullest potential.

• Understanding of accessibility and security compliances

• Ability to communicate complex technical concepts to both technical and non- technical audiences with ease

• Diversity in skills like version control tools, CI/CD, cloud basics, good debugging skills, and test-driven development will help your profile stand out. 

Job Description

• Role: Quality Assurance Engineer – Automation (3–4 yrs)

• Location: Bengaluru

• Type: Full-time

Why this role? Join a fast-moving team that’s pushing test automation into the AI era. You’ll own end-to-end quality for web, mobile and API layers, combining Playwright (or similar) with next-gen, AI-driven test platforms to deliver smarter, faster releases.

What you’ll do

• Build & maintain automation with Playwright, Selenium, Cypress or equivalent

• Super-charge coverage using AI-powered tools

• Create, run and optimize manual, API (Postman/Rest Assured) and database (SQL) tests

• Triage results, file defects in Jira, and drive them to closure What you bring

• 3–4 years’ hands-on automation experience

• Strong with Playwright (preferred) or Selenium/Cypress and one scripting language (JS/TS, Python or Java)

• Familiarity with AI-based testing platforms

• Solid API testing & SQL skills; sound grasp of STLC and defect management

• Clear communicator with sharp analytical instincts

• Nice to have: BDD (Cucumber/SpecFlow), performance testing (JMeter/LoadRunner), TestRail/Zephyr, ML model validation Qualifications Bachelor’s in Computer Science, Engineering or related field What’s in it for you?

• Hands-on exposure to cutting-edge AI test automation

• Ownership and room to innovate in a collaborative, high-impact environment

• Competitive pay, flexible policies and a fun teaM

Responsibilities

• Model Development and Optimization: Design, build, and deploy NLP models, including transformer models (e.g., BERT, GPT, T5) and other SOTA architectures, as well as traditional machine learning algorithms (e.g., SVMs, Logistic Regression) for specific applications.

• Data Processing and Feature Engineering: Develop robust pipelines for text preprocessing, feature extraction, and data augmentation for structured and unstructured data.

• Model Fine-Tuning and Transfer Learning:

Fine-tune large language models for specific applications, leveraging transfer learning techniques, domain adaptation, and a mix of deep learning and traditional ML models.

• Performance Optimization: Optimize model performance for scalability and latency, applying techniques such as quantization, ONNX formats etc.

• Research and Innovation: Stay updated with the latest research in NLP, Deep Learning, and Generative AI, applying innovative solutions and techniques (e.g., RAG applications, Prompt engineering, Self-supervised learning).

• Stakeholder Communication: Collaborate with stakeholders to gather requirements, conduct due diligence, and communicate project updates effectively, ensuring alignment between technical solutions and business goals.

• Evaluation and Testing: Establish metrics, benchmarks, and methodologies for model evaluation, including cross-validation, and error analysis, ensuring models meet accuracy, fairness, and reliability standards. • Deployment and Monitoring: Oversee the deployment of NLP models in production, ensuring seamless integration, model monitoring, and retraining processes.