Senior Agentic Harness Engineer

The client is building a governed, AWS-native agentic harness that runs their software delivery loop autonomously: JIRA tickets trigger epics and stories, which trigger agents for design, coding, review, and testing. This is not a prototype exercise; the client's engineering team already uses Claude Code and similar tools, and this role is about turning that into a production-grade, auditable, cost-controlled system. Resources will be embedded in their team, pairing with their internal engineers so capability transfers as they go. Resources will work alongside a mix of off-the-shelf tooling and custom-built components.

Responsibilities:

• Build and operate the agentic loop: trigger orchestration agent execution output to JIRA; human accept/reject next agent across design, coding, review, and testing agents.
• Implement model routing and retry logic across a provider-agnostic model layer (e. g., Claude via AWS Bedrock, self-hosted or alternative models as cost/sovereignty hedges), including business-continuity fallback if a given provider becomes unavailable.
• Own token cost control and context window management per-agent and per-run budgets, circuit breakers that halt runaway execution, and cost observability tied back to JIRA.
• Stand up and maintain observability, alerting, and monitoring across the agent fleet (e. g., Langfuse or equivalent), so agent health, cost, and quality are visible in real time.
• Implement agent governance and safety guardrails: deterministic pre/post hooks gating every LLM call, kill switches, prompt injection prevention and mitigation, and audit logging.
• Integrate the harness with JIRA as the system of record and other business systems as needed, ensuring every agent action, decision, and human override is tracked with no side channels.
• Pair directly with client engineers; this is capability transfer, not black-box delivery. You'll document, demo, and hand over as you build.
• Work in outcome-based delivery stages (spike, architecture sign-off, build pilot) with gated milestones tied to working software demos, not fixed artifact checklists.
• Participate actively in team discussion and design decisions; this team expects engineers to challenge ideas constructively and speak up, not defer silently.

Requirements:

• Bachelor's degree in computer science, engineering, or equivalent practical experience.
• 3-5+ years in software/platform engineering, with a meaningful portion of that time specifically on agentic or LLM-orchestration systems (not general ML or data engineering alone).

Must-Have Experience:

• Hands-on production experience building agentic systems (not tutorial-level or personal-project experience). Candidates should be able to speak concretely about systems they've shipped.
• Practical experience with agentic frameworks such as LangChain, LangGraph, or equivalent orchestration frameworks.
• Experience with LLM orchestration and model routing across multiple providers/models, including fallback and retry design.
• Working knowledge of agent governance: guardrails, human-in-the-loop approval flows, kill switches, and audit trails.
• Practical understanding of prompt injection risks and mitigation techniques.
• Experience with token cost management and context window/memory handling at production scale: this is a named governance requirement for the engagement, not a nice-to-have.
• Strong Python (or equivalent) engineering background, comfortable working in AWS environments (Bedrock/AgentCore exposure a strong plus).
• Experience with observability/monitoring tooling for distributed or agentic systems (e. g., Langfuse, Datadog, or equivalent).
• Comfortable working with JIRA/Atlassian APIs or similar ticketing-system-of-record integrations.

Nice to Have:

• Direct experience with AWS Bedrock AgentCore, Temporal (or similar workflow orchestration), or LiteLLM-style model gateways.
• Exposure to Cursor or other AI-native IDEs in a production engineering context.
• Experience with self-hosted open-weight models (e. g., DeepSeek, GLM) as cost or sovereignty hedges alongside commercial APIs.
• Financial services or other regulated-industry background.
• Familiarity with Claude Code, Claude Cowork, or Claude Skills.

Relevant Experience:

• Already built this kind of system and can talk through the trade-offs from experience, not theory.
• Comfortable operating with ambiguitytechnology choices (frameworks, specific models, tooling) are expected to evolve during the engagement, and milestones are tied to outcomes rather than fixed deliverables.
• Will contribute opinions; quiet execution without a point of view is not a fit for this team.