Zensar Technologies—AI Central - Senior AI Engineer

Job description

Description: 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. Requirements: 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. Responsibilities: 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.