Senior Software Engineer - AI Innovation
New
W
Worth AIAI fintech
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
- Required Skills
- Node.jsPythonKubernetesTypeScriptMLOps
Requirements
- 8+ years of professional software engineering experience.
- At least 2 years building production LLM or agentic systems, beyond notebooks or demos.
- Strong software engineering experience across front-end, APIs, asynchronous patterns, queues, databases, and distributed systems failure modes.
- Demonstrated ownership of major production features or subsystems.
- Experience mentoring junior engineers and raising team quality standards.
- Experience with event-driven systems, including enrichment, retries, dead-lettering, and backpressure.
- Experience managing containerized applications with Kubernetes, EKS, ArgoCD, operators, and Kustomize.
- Hands-on experience with a modern agent framework; LangGraph is strongly preferred.
- Experience building golden sets, running offline and online evaluations, and using results for ship/no-ship decisions.
- Production MLOps experience deploying instrumented LLM workloads under latency, cost, and reliability constraints.
- Strong proficiency in Python and comfort with TypeScript / Node.js.
- Ability 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 using chunking strategies, hybrid search, reranking, and grounded citations.
- Own the evaluation stack, including golden sets, offline regression suites, online evaluations, and red-teaming.
- Integrate agents with production systems through typed tools, MCP servers, and existing services.
- Drive agent MLOps, including deployment, versioning, traffic shaping, cost and latency budgets, and observability.
- Partner with security, compliance, and legal to maintain SOC 2, GDPR, CCPA, and fair-lending safeguards.
- Translate product concepts into agent designs, prototypes, and shipped features.
- Mentor engineers on agent patterns, prompt engineering hygiene, evaluation discipline, and LLM failure modes.
- Evaluate new models, frameworks, and patterns and identify approaches that work in production.
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