Position Responsibilities:
You will primarily execute within defined frameworks and grow your independent scope over time. You expected to independently build and deploy production-grade AI solutions. The following reflects what you will own and contribute to:
Build & Integrate
- Build and deploy AI-powered applications: Design, develop, and maintain AI-first applications, AI agents, agentic workflows, automation solutions, and platform integrations using Python and frameworks such as LangChain, LlamaIndex, n8n, or equivalent technologies.
- Develop LLM-powered solutions: Apply hands-on experience with Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), vector databases, embeddings, and AI agent frameworks to build reliable, context-aware applications.
- Engineer agentic workflows: Implement tool calling, multi-step workflows, workflow orchestration, and integrations that enable AI agents to retrieve information, execute tasks, and interact with enterprise systems. Familiarity with using AI powered coding tools like Claude Code, Cursor, Codex etc.
- Build for production: Develop modular, scalable, secure, and maintainable software. Contribute across the development lifecycle, from rapid prototyping and proof of concept through testing, deployment, and ongoing optimization of production applications.
- Apply modern DevOps practices: Use GitHub, CI/CD pipelines, Docker, Kubernetes, and cloud platforms to support version control, automated testing, deployment, and operational reliability.
- Leverage AI-assisted development: Effectively use AI-powered coding tools such as Claude Code, Cursor, Codex, or equivalent tools to accelerate development, improve code quality, and increase engineering productivity.
- Develop APIs and enterprise integrations: Build and integrate REST APIs, backend services, and data pipelines connecting platforms, and internal enterprise systems.
- Collaborate across teams: Work with AI engineers, product specialists, and business stakeholders to translate customer and operational needs into practical, scalable AI solutions.
Analyse & Solve”
- Break down defined problems into actionable tasks; identify risks, dependencies, and edge cases before they surface in production
- Troubleshoot complex issues across the full stack and document root cause findings clearly
- Investigate stakeholder-reported issues to identify whether the problem is technical, process-related, or both.
Operate & Improve
- Follow established governance, architecture, and deployment processes; raise improvement suggestions through proper channels
- Write and maintain documentation for systems, workflows, business rules, and solution designs
- Participate actively in sprint ceremonies; manage your own tasks and flag blockers early
- Demonstrate continuous learning in AI, automation, and integration technologies — this space moves fast and curiosity is part of the job.
Nice-to-Have Skills:
The following are not required for the role, but candidates with depth in any of these areas will stand out. Listed in rough order of relevance to this team’s current work:
|
Oracle Service Cloud |
Workato / iPaaS |
Salesforce |
|
Gainsight |
AI / LLM integration |
Snowflake |
|
Microsoft Power BI |
Microsoft Power Apps |
Cloud-native development |
Qualifications
- Education: Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related technical discipline. Equivalent practical experience considered.
- Experience: 4–6 years of hands-on experience in software engineering, systems integration, or a closely related field. Internship and co-op experience counts if it involved real production systems.
- Coding: Demonstrable Python, and/or JavaScript experience — a portfolio, GitHub profile, or code sample you can speak to will strengthen your application.
- Collaboration model: Comfortable working remotely with distributed teams. The role requires regular overlap with US East/Central time zones (approximately 2:00 PM – 11:00 PM IST for at least part of the week).
- Language: Strong written and spoken English is essential — much of the collaboration with stakeholders and senior engineers happens asynchronously in writing.
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