Sr Agentic Harness Engineer

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 throughout — 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. 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. Qualifications · 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). Relevant Experience · Already built this kind of system and can talk through the trade-offs from experience, not theory. · Comfortable operating with ambiguity - technology choices (frameworks, specific models, tooling) are expected to evolve during the engagementand 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. Skills:- AWS Bedrock, Agentic AI, Harness, Python, JIRA, LLM Evaluation Frameworks, IT governance and AgentCore,