Senior Software Engineer - AI Innovation
New
W
Worth AIAI financial technology
Workable workplace: remote. Workable locations: Orlando, Florida, United States. Atlanta, Georgia, United States. Tampa, Florida, United States. Miami, Florida, United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8+ years of professional software engineering experience, with at least 2 years building production LLM or agentic systems (not just notebooks or demos).
- Required Skills
- Node.jsPythonKubernetesTypeScript
Requirements
- Have 8+ years of professional software engineering experience.
- Have at least 2 years of experience building production LLM or agentic systems, beyond notebooks or demos.
- Bring software engineering experience across front-end, APIs, asynchronous patterns, queues, databases, and distributed-system failure modes.
- Have owned major production features or subsystems.
- Have experience mentoring junior engineers and raising team quality standards.
- Have experience with event-driven systems, including enrichment, retries, dead-lettering, and backpressure.
- Have experience managing containerized applications with Kubernetes, EKS, ArgoCD, operators, and Kustomize.
- Have hands-on experience with a modern agent framework; LangGraph is strongly preferred.
- Have built evaluation sets and run offline and online evaluations to inform release decisions.
- Have production MLOps experience deploying LLM workloads under latency, cost, and reliability constraints, with instrumentation.
- Be strongly proficient in Python and comfortable using TypeScript and Node.js to integrate with existing services.
- Be able to explain agent trade-offs to product, security, and customers.
Responsibilities
- Design and ship multi-step agentic systems for KYB, underwriting, case review, and risk monitoring workflows.
- Architect agent graphs with explicit state, durable execution, retries, and safe fallbacks.
- Build and harden retrieval systems using chunking, hybrid search, reranking, and grounded citations.
- Own evaluation systems, including golden sets, offline regression suites, LLM-as-judge, online A/B and shadow evaluations, and red-teaming.
- Integrate agents with production systems through typed tools, MCP servers, and existing services.
- Drive production MLOps, including deployment, versioning, traffic shaping, cost and latency budgets, observability, and incident playbooks.
- Partner with security, compliance, and legal to maintain SOC 2, GDPR, CCPA, and fair-lending requirements.
- Translate product concepts into agent designs, prototypes, and shipped features.
- Mentor engineers on agent patterns, prompt engineering hygiene, evaluation discipline, and LLM system failure modes.
- Evaluate new models, frameworks, and patterns for production use.
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