Senior Software Engineer AI (Contractor)

Agentic & AI Engineering

• • Design, build, and operate production-quality agentic applications using agentic SDKs/harnesses — including multi-agent orchestration, subagent delegation, tool integration, and lifecycle hooks.
  • Design Model Context Protocol (MCP) tool and resource interfaces that give coding agents/harnesses safe, reliable access to backend systems and enterprise data.
  • Engineer prompts for reliable structured output (JSON schemas, few-shot examples, validation/retry loops) and build evaluation frameworks to measure and improve output quality.
  • Manage context effectively across long documents, multi-turn conversations, and multi-agent handoffs; design escalation and human-in-the-loop patterns for production reliability.

Developer Productivity

• • Configure and extend Codex/Claude Code for team-wide use: AGENTS.md/CLAUDE.md hierarchies, custom slash commands, Agent Skills, path-specific rules, and plan mode.
  • Integrate agentic development into CI/CD pipelines for automated code review, test generation, and PR feedback, tuned to minimize false positives and duplicate findings.
  • Build and maintain MCP server integrations (project- and user-scoped) that extend agent-native development capabilities across engineering workflows.
  • Identify high-value opportunities for AI-assisted development and design reusable capabilities that multiple teams can adopt, not just one-off solutions.

Discovery, Prototyping & Production Delivery

• • Partner directly with engineering teams to understand workflows, pain points, and opportunities through hands-on discovery.
  • Rapidly prototype and validate new AI capabilities with real users, then take successful prototypes into secure, observable, maintainable production systems.
  • Own the full lifecycle: implementation, testing, deployment, monitoring, and iteration.

Technical Leadership

• • Provide hands-on technical leadership through architecture discussions, code reviews, and troubleshooting of agentic systems.
  • Mentor engineers on agentic design patterns, prompt engineering, and agent-native development practices.
  • Communicate tradeoffs, risks, and results clearly to both technical and non-technical stakeholders.
  • Contribute to engineering standards, reusable patterns, and documentation that help other teams build effectively with agentic development tools.

Requirements

• • 6+ years of professional software engineering experience, including significant hands-on work building and operating AI/LLM-powered or agentic applications.
  • Strong programming skills (Java, Python, Go, Rust, or C++) and solid engineering fundamentals — API design, testing, debugging, code review, maintainable code.
  • Practical, production-grade experience with agentic SDKs/harnesses, MCP, and LLMs — tool use, orchestration, RAG, context management, structured output, and evaluation.
  • Experience designing MCP tool/resource interfaces and reasoning about tool selection, error handling, and reliability in multi-agent systems.
  • Demonstrated experience integrating AI into CI/CD and developer workflows.
  • Ability to work in ambiguous problem spaces, evaluate tradeoffs, and make pragmatic engineering decisions.
  • Strong written and verbal communication skills, with experience mentoring and leading through influence.
  • Build and maintain an engineering knowledge base for agentic workflows, including content ingestion, organization, updates, and retrieval. Connect it to agents through RAG and MCP so they can use trusted, current information with source citations.

Preferred Qualifications

• • Demonstrated expertise across agentic architecture, MCP integration, Codex/Claude Code configuration, prompt engineering, and context management.
  • Experience building multi-agent research or customer support systems, or structured data extraction pipelines.
  • Experience with AWS, Docker, Kubernetes, Terraform, and GitOps in support of AI/agentic infrastructure.
  • Familiarity with vector databases, embeddings, and AI evaluation frameworks.
  • Contributions to open-source projects involving AI, agentic systems, or developer tooling.
  • Strong systems thinking — understands how models, agents, tools, context, APIs, data, and infrastructure fit together as production systems, and can reason about dependencies and tradeoffs across the stack.
  • A track record of adapting quickly to new AI tools, models, and frameworks as the space evolves.