The Context & Harness Committee is moving from strategy to execution. We are seeking three hands-on AI Tech Leads to define and validate our context engineering standards and evaluation harness before company-wide rollout.

Core responsibilities
Lead the technical design and delivery of the assigned Context and Harness workstreams
Review context formats, ontology models, harness proposals, and integration approaches.
Support pilot teams across MFBS divisions with context conversion, fixture authoring, and evaluation.
Define how GitHub, Notion, and Slack content maps into the agent context engine.
Establish guidelines for federated context routing and token-budget allocation across sources.
Contribute to an ontology proof of concept covering one domain and one workflow using YAML-based schemas.
Validate integration with internal or external agent platforms, including access and smoke testing.
Capture pilot evidence and contribute to the standard and company best-practices package.

Requirements
Required skills

Context engineering skills




RAG and retrieval pipelines:Design advanced search and retrieval systems that supply models with accurate, relevant, and timely information.
Context-window and token management:Compress conversation history, prioritize practical context, and filter noise to optimize model attention and cost.
Memory architecture:Build short-term and persistent memory systems that allow agents to retain state across tasks and interactions.
Instruction curation:Structure plain-English operating rules, Markdown files, domain guides, and other instructions for agile injection into agent tasks.
Context formats and knowledge modeling:Design context layers and reusable knowledge assets using Markdown, YAML, schemas, and related formats.

Harness engineering skills
Tool and MCP integration: Connect models to external APIs, execution settings, tools, and Model Context Protocol (MCP) servers.
Sandboxing and permissions: Define s

📌 Ai Senior Engineer Tech Lead Chennai (India)
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