Senior Software Engineer (Pune, India)
As a Lead Developer, you’ll drive the creation of new software products and enhancements to our existing platform. Description Palosade is on a mission to help companies protect their operations and customers by advancing the state of AI security. The ideal candidate is a self-motivated team player and proven leader. As a Lead Developer, you’ll drive the creation of new software products and enhancements to our existing platform. You thrive in large-scale environments, communicate clearly across teams, and inspire engineering excellence. Location In-person in Pune, India Responsibilities Invent, design, and implement scalable, efficient, and reliable solutions Translate user stories into rapid prototypes and production-ready features—estimating effort, iterating with the product team, writing tests, and shepherding code through review Own end-to-end delivery: continuous integration, automated regression testing, and smooth deployments while maintaining and refactoring our existing codebase Collaborate in a dynamic, iterative environment where innovation and feedback fuel our success Evolve and document our architecture to ensure robustness, performance, and security Partner with UX/UI designers to shape intuitive workflows and polished interfaces Qualifications 10+ years of professional software development experience 5+ years of full-stack development, with deep fluency in modern JavaScript/TypeScript and at least one backend language (e.g., Python, Java) 5+ years building services with frameworks such as Next.js, Node.js, Flask, or Spring 3+ years with front-end frameworks like React or Angular 3+ years architecting and operating solutions in public clouds (AWS, GCP, or Azure) Strong grasp of OWASP Top 10 and secure coding best practices Bachelor’s degree in Computer Science or a related discipline AI/ML Expertise Hands-on experience building AI/ML-driven applications leveraging large language models (LLMs) Familiarity with foundational model families (e.g., GPT, LLaMA, PaLM) and their deployment considerations Proven skills in data preparation, prompt engineering, fine-tuning, or training custom models on domain-specific data Understanding of MLOps workflows: model versioning, evaluation metrics, monitoring, and continuous improvement
