AI Senior Engineer Tech lead

• 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

• 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.
  

• 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
  

• 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.
  

• 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.