Draup
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Python
JavaWe 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.
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PythonJavaJob 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
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Machine Learning (ML)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.
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