Live opening · Posted 7 hours ago
AI Central - Senior AI Engineer
Zensar Technologies · India
You are 7 hours behind. JobBeeper subscribers saw this role while it was still new.
At a glance
The key details from the original listing.
CompanyZensar Technologies
LocationIndia
Source
Oracle
Listed7 hours ago
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About the role
Description supplied by the original job listing.
Authoring repository-level context for AI coding agents: instruction files, skills, prompt libraries, and the conventions that make them consistent across many repositories.
• Reading and reasoning about unfamiliar production code in at least two languages well enough to describe what it does and why.
• Automated documentation and code-comprehension generation across legacy codebases.
• Context and retrieval design: what to include, what to exclude, chunking and indexing, grounding agent output in real repository facts.
• Strong technical writing for a developer audience, and the discipline to templatize rather than hand-craft each product.
• Evidence of measurably improving agent output quality by improving context, not by changing the model.
Nice To Have
Exposure to legacy stacks — COBOL in particular — where added application and domain context is what makes agentic work viable.
• AST, code-graph or static-analysis tooling used to generate context automatically.
• Information architecture or taxonomy background.
Nice To Have
Exposure to legacy stacks — COBOL in particular — where added application and domain context is what makes agentic work viable.
• AST, code-graph or static-analysis tooling used to generate context automatically.
• Information architecture or taxonomy background.
Authoring repository-level context for AI coding agents: instruction files, skills, prompt libraries, and the conventions that make them consistent across many repositories.
• Reading and reasoning about unfamiliar production code in at least two languages well enough to describe what it does and why.
• Automated documentation and code-comprehension generation across legacy codebases.
• Context and retrieval design: what to include, what to exclude, chunking and indexing, grounding agent output in real repository facts.
• Strong technical writing for a developer audience, and the discipline to templatize rather than hand-craft each product.
• Evidence of measurably improving agent output quality by improving context, not by changing the model.
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