Principal Software Engineer

India - Bangalore · Bangalore · 10+ years experience · Posted Oct 5, 2026

At a glance

As a Principal Software Engineer within the Document Automation pillar at Conga, you will lead the practical adoption of AI-enabled engineering practices across multiple products, services, and teams.
Your focus will be improving how software is designed, built, tested, reviewed, documented, released, and supported by combining hands-on engineering and prototyping with broad technical leadership.

Define and drive an AI-enabled engineering productivity strategy across multiple Document Automation products and services.
Identify and prioritize opportunities to apply AI across discovery, requirements refinement, architecture, coding, testing, code review, documentation, release, troubleshooting, and support workflows.
Build prototypes, reference implementations, reusable accelerators, integrations, and agentic workflows that teams can adopt at scale.
Establish practical standards, guardrails, and secure usage patterns for AI-assisted software engineering.
Partner with engineering leaders and teams to integrate successful practices into everyday delivery workflows, CI/CD pipelines, and quality processes.
Define baselines and success measures, evaluate adoption and outcomes, and use evidence to continuously improve approaches.
Mentor Staff, Senior, and other engineers; facilitate hands-on learning; and build a sustainable community of practice in India.
Influence technical decisions across services and systems, build consensus where approaches differ, and communicate recommendations to engineering and product leadership.
Remain hands-on by contributing code, reviewing designs, troubleshooting complex issues, and demonstrating new development techniques in real product environments.

Principal-level technical leadership with a record of leading complex initiatives across multiple products, services, or engineering teams.
Practical experience applying generative AI, large language models, coding assistants, agentic workflows, retrieval techniques, or related technologies to improve software engineering outcomes.
10+ years of software engineering experience and a strong track record designing, building, and operating enterprise-grade SaaS solutions.
Strong proficiency in C# and .NET, experience with REST APIs and distributed systems, and practical experience with modern web application frameworks such as React or comparable technologies.
Meaningful experience building on AWS or Azure and understanding how frontend, backend, platform, and delivery concerns work together.
Deep knowledge of CI/CD, automated testing, observability, secure development, code quality, and agile delivery.
Ability to evaluate privacy, security, intellectual property, reliability, and governance considerations and translate them into practical engineering guardrails.
Ability to define useful baselines and outcome measures, distinguish activity from impact, and prioritize solutions based on value, usability, adoption, and maintainability.
Ability to communicate complex technical ideas clearly to engineers, leaders, and cross-functional stakeholders.

Experience leading AI adoption or developer productivity initiatives in a large engineering organization.
Hands-on experience with tools and platforms such as GitHub Copilot, Azure Foundry, Azure OpenAI, Amazon Bedrock, OpenAI APIs, or comparable technologies.
Experience designing AI agents, tool integrations, evaluation frameworks, prompt and context strategies, or retrieval-augmented solutions.
Experience with developer platforms, internal tooling, CI/CD automation, frontend and API test generation, code review automation, knowledge systems, or engineering analytics.
Experience working across modern web interfaces, service APIs, backend systems, and cloud infrastructure without being limited to a single layer of the stack.
Experience supporting large-scale, multi-tenant SaaS products and modernizing established codebases and engineering workflows.
A demonstrated ability to coach others and turn experimentation into repeatable, broadly adopted engineering practices.