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Staff AI Software Engineer
Gevernova
Bengaluru, IND
Full-time
Onsite
Discovered 2 weeks ago
AI platform architectureAI agentsModel Context Protocol (MCP) serversLLM and generative AI integrationRetrieval-augmented generation (RAG)Prompt engineering and structured prompting
Free
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AI platform architectureAI agentsModel Context Protocol (MCP) servers
Role Overview
Lead the architecture, development, deployment, and end-to-end lifecycle of an enterprise AI platform and its underlying infrastructure.
Balance high-level system strategy with hands-on technical execution to deliver scalable, secure, and integrated agentic workflows.
Key Skills for This Role
AI platform architectureAI agentsModel Context Protocol (MCP) serversLLM and generative AI integrationRetrieval-augmented generation (RAG)Prompt engineering and structured prompting
Full Job Posting
Role Overview
Lead the architecture, development, deployment, and end-to-end lifecycle of an enterprise AI platform and its underlying infrastructure.
Balance high-level system strategy with hands-on technical execution to deliver scalable, secure, and integrated agentic workflows.
Responsibilities
- Design and deploy AI agents, MCP servers, tools, and LLM-powered product capabilities.
- Build AI-native applications and implement structured prompting, memory management, context management, and RAG patterns.
- Drive medium-to-large feature delivery, technical direction, mentoring, technical reviews, integrations, and contractor guidance.
- Establish prompt and model-performance testing, observability, Agile practices, and production support.
Required Qualifications
- Bachelor’s degree in computer science, computer engineering, or a related technical field.
- 6+ years using at least one general-purpose programming language, with JavaScript or TypeScript front-end experience.
- Production experience shipping applications that use LLMs, MCPs, or AI services at scale.
- Experience leading technical teams, guiding contractors or vendors, and overseeing code quality and assurance.
- Strong experience with AWS, GCP, or Azure and advanced Docker and Kubernetes containerization.
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