AI Senior Engineer Tech lead

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 useful 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 dynamic 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 environments, tools, and Model Context Protocol (MCP) servers.
• Sandboxing and permissions: Define safety boundaries, access controls, approval flows, and secure execution environments for agent actions.
• Validation and guardrails: Implement linters, automated tests, evaluations, and verification layers that detect hallucinations, policy violations, or broken code.
• Orchestration and control loops: Design multi-step workflows, retry logic, state transitions, and error-correction loops for long-horizon tasks.
• Evaluation harnesses: Build fixtures, benchmark tasks, quality gates, and CI-integrated evaluations using DeepEval, Promptfoo, or comparable frameworks

Supporting technical and leadership skills

• LLM and agent fundamentals: Strong understanding of agent workflows, RAG, evaluation methods, and quality gates for AI-generated output.
• Software engineering: Strong Git/GitHub and CI practices, experience with repo-native tooling, and the ability to prototype quickly.
• Technical leadership: Ability to make pragmatic architecture decisions, lead POCs, and turn evidence into reusable standards.
• Communication: Ability to write clear technical proposals and provide constructive cross-team review and feedback.
• Experience with ontology or knowledge modeling using YAML-based schemas.
• Familiarity with internal or external agent platforms.
• Experience with hybrid search, indexing pipelines, or context freshness mechanisms.

Success outcomes

• Context architecture, format, and evaluation standards are validated through representative pilots.
• Pilot teams can convert source knowledge and author evaluation fixtures using documented playbooks.
• A working repo-native harness POC demonstrates repeatable benchmark execution and quality gates.
• The committee has sufficient evidence to ratify the  standard and decide whether to adopt or build the long-term harness.