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About This Role
Worth AI, a leader in AI onboarding and underwriting, is looking for a talented and experienced Senior Software Engineer - AI Innovation to join our team. At Worth AI, we are on a mission to revolutionize decision-making with the power of artificial intelligence helping fintechs, lenders, payment processors, and financial institutions onboard small businesses faster, smarter, and more confidently. We’re building the infrastructure that powers real-time KYB, KYC/IDV, underwriting, and continuous risk monitoring at enterprise scale, and the Worth Score™ our unified credit score derived from 1,200+ data points across 700M+ SMBs.
As a Senior Software Engineer - AI Innovation, you won’t be wiring up demos you’ll be designing and shipping production agent systems that make consequential decisions on regulated financial data. Worth’s platform consolidates onboarding and underwriting into a single AI-powered system, and our agents read, reason over, and act on the messy, high-stakes signals that come with that domain. You’ll own the end-to-end lifecycle: architecting agent graphs, building the retrieval and tool layers they rely on, instrumenting them with evals and observability, and getting them deployed against SOC 2 / GDPR / CCPA guardrails. You’ll partner closely with our Chief AI Officer, applied scientists, product, and platform teams to turn agentic patterns into customer outcomes.
Responsibilities* Design and ship multi-step agentic systems (planner/executor, tool-using, multi-agent, human-in-the-loop) that automate KYB, underwriting, case review, and risk monitoring workflows.
- Architect agent graphs in LangGraph (or comparable frameworks CrewAI, AutoGen, Claude Agent SDK) with explicit state, durable execution, retries, and safe fallbacks.
- Build and harden the retrieval layer powering our agents chunking strategies, hybrid search, reranking, and grounded citation across SoS filings, IRS records, bank data, and Worth’s 700M+ SMB graph.
- Own the eval stack: golden sets, offline regression suites, LLM-as-judge, online A/B and shadow evals, and red-teaming for jailbreaks, prompt injection, and PII leakage.
- Wire agents into Worth’s production systems via well-typed tools, MCP servers, and existing services (decisioning engine, case management, crosswalking). Treat tool surface area as a product.
- Drive production MLOps for agents: deployment, versioning, traffic shaping, cost/latency budgets, observability (traces, token spend, tool call success), and on-call playbooks for agent incidents.
- Partner with security, compliance, and legal to keep agents inside Worth’s SOC 2, GDPR, CCPA, and fair-lending posture — building from day one, not bolted on.
- Translate ambiguous product bets (“what if the underwriter had an AI co-pilot for this?”) into concrete agent designs, prototypes, and shipped features.
- Mentor engineers across the org on agent patterns, prompt engineering hygiene, eval discipline, and the failure modes of LLM systems.
- Stay ahead of the frontier new models, frameworks, and patterns — and bring back what actually works in production.
Technology Stack
- Languages & Runtimes: Python, Node.js, TypeScript
- Agent / LLM frameworks: LangGraph, LangChain, Claude Agent SDK, MCP, OpenAI SDK
- Models: Anthropic Claude, OpenAI, open-weight (Llama, Mistral) where appropriate
- Retrieval & Data: PostgreSQL, pgvector / vector DBs, OpenSearch, Kafka, Redshift, Redis
- Infra & Orchestration: AWS, Kubernetes (EKS), ArgoCD, Terraform
- Evals & Observability: LangSmith / Langfuse / Braintrust-style tooling, DataDog, custom eval harnesses
Requirements
- 8+ years of professional software engineering experience, with at least 2 years building production LLM or agentic systems (not just notebooks or demos).
- Solid software engineering experience - front-end, APIs, async patterns, queues, databases, and the failure modes of distributed systems.
- Demonstrated ownership of major features or subsystems in production.
- Demonstrated experience mentoring junior engineers and raising team quality standards.
- Demonstrated experience with event-driven systems: enrichment, retries, dead-lettering, backpressure.
- Experience managing containerized applications in Kubernetes, EKS, ArgoCD, operators, Kustomize.
- Deep, hands-on experience with at least one modern agent framework (LangGraph strongly preferred) and a track record of shipping agents that actually run, fail gracefully, and recover.
- Real experience with evals you’ve built golden sets, run offline and online evaluations, and used them to make ship/no-ship calls.
- Production MLOps fluency: you’ve deployed LLM workloads under real latency, cost, and reliability constraints, and you instrument what you ship.
- Strong proficiency in Python; comfortable in TypeScript / Node.js for integrating with Worth’s services.
- Clear, calibrated communicator - able to explain agent trade-offs to product, security, and customers without hand-waving.
- Operates with extreme ownership in ambiguous, fast-moving environments. Excited to work alongside a team that values “One Team”, “Extreme Ownership”, and “Create Raving Fans.”
Success Metrics
- Agent Quality: Measurable improvements in task success rate, grounding accuracy, and hallucination rate on Worth’s eval suites, tied to customer-visible outcomes.
- Production Reliability: Agents you own meet defined SLOs for latency (P90/P99), tool-call success rate, and cost per task.
- Velocity: New agent capabilities go from prototype to production in weeks, not quarters, without skipping evals or guardrails.
- Risk Posture: Zero material incidents tied to prompt injection, PII leakage, or unsafe tool use on agents you own.
- Force Multiplier: Patterns, tools, and eval scaffolding you build are adopted by other engineers across Worth.
Bonus Points (nice to haves, not requirements)
- Prior experience in fintech, lending, payments, KYB/KYC, fraud, or AML — or any other regulated, high-stakes data domain.
- Experience building MCP servers or other structured tool interfaces for LLMs.
- Background in classical ML (ranking, scoring, calibration) you can bring to bear alongside LLM systems.
- Experience designing explainable / auditable AI workflows for regulated environments (SOC 2, model risk management, fair lending).
- Open-source contributions to agent frameworks, eval tooling, or retrieval libraries.
- Hands-on AWS depth (EKS, MSK, RDS, S3, Lambda) and IaC with Terraform.
**All Remote Hires — will be required to travel to Orlando, Florida at least twice per year for Town Halls and team collaboration, in addition to orientation in Orlando, Florida.
Benefits
- Health Care Plan (Medical, Dental & Vision)
- Retirement Plan (401k, IRA)
- Life Insurance
- Flexible Paid Time Off
- 9 paid Holidays
- Family Leave
- Remote
- Hybrid work (for Orlando Associates)
- Free Food & Snacks (Orlando)
- Wellness Resources
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Worth AI, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Worth AI AI Hiring
Worth AI has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Tampa, FL, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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