• Deliver phase-one AI capabilities in our existing Amazon Connect environment, focusing on AI-first call handling, intelligent routing, and autonomous resolution of common login, account, and password-reset queries. • Design AI-first workflows for voice and email that prioritize autonomous resolution, allowing the system to intelligently decide when to handle a request end-to-end and when to route to an agent. • Leverage our existing call recordings and transcripts to extract real interaction patterns and customer language to ground the AI in actual member needs. • Build elegant handoff patterns that preserve a member's interaction history, intent, and attempted actions so they never have to repeat themselves. • Write clean, production-ready Python backend code and AWS Lambda integrations to handle secure account lookups, validation, and CRM updates. • Integrate Amazon Connect workflows with HubSpot and other internal systems to equip the AI agent with real-time customer context. • Establish a reusable metadata and session strategy to enrich ongoing and future interactions with purchase history and communication logs. • Tune RAG (Retrieval-Augmented Generation) patterns and Bedrock Knowledge Bases to keep AI responses grounded in approved policies and procedures. • Implement practical guardrails that keep AI responses focused on approved topics and apply secure verification steps for sensitive workflows. • Deploy and manage AWS resources using CloudFormation, CDK, or Terraform, ensuring version-controlled deployments of Connect flow JSONs. • Author clean, clear architecture runbooks, deployment procedures, and prompt configurations to ensure the wider team can easily operate your work. • Lay the technical foundations for future authenticated transactions like billing updates, self-service cancellations, and payment processing.

Role Description Our client is a fast-growing, technology-driven specialty insurance platform that is transforming how commercial insurance products are built, distributed, and serviced. The company is investing heavily in AI-enabled engineering practices and is looking for a hands-on Senior Staff Engineer who can help raise the technical bar, accelerate delivery, and guide small engineering pods through complex product work. This is a hands-on technical leadership role for someone who enjoys building, designing, and shipping software - not just advising from a distance. You will: - Lead complex projects end-to-end. - Write production code. - Guide engineers. - Partner closely with product and business stakeholders. - Help the team adopt more advanced AI-assisted development practices. - Act as a technical lead within small project-based pods. - Assist with architecture, task breakdown, code quality, delivery execution, and team mentorship. Qualifications - 10+ years of professional software engineering experience. - Strong hands-on full stack engineering background, with heavier depth in backend development. - Advanced experience with TypeScript and Node.js. - Experience with backend frameworks such as NestJS, Express, or similar. - Strong experience designing and building REST APIs and third-party integrations. - Solid frontend familiarity with React. - Experience working in AWS environments. - Experience with NoSQL databases such as DynamoDB. - CI/CD experience, preferably with GitHub Actions or similar tools. - Container experience such as ECS, Docker, or Kubernetes. - Proven ability to design solutions end-to-end, not just execute pre-defined requirements. - Experience acting as a technical lead for small engineering teams or project pods. - Strong ability to work independently in a lean, fast-moving engineering environment. - Strong product mindset and comfort working directly with business and product stakeholders. - Ability to explain technical concepts clearly to both technical and non-technical audiences. - Practical experience using AI coding tools beyond basic prompt-and-response usage. - Experience introducing AI-assisted development practices to an engineering team. - Architecture or solution design experience close to architect level. Requirements - Full-Time Permanent employment. - Remote, but MUST be physically located in the US. - Compensation: USD $150,000-175,000 base + bonus + full benefits. Benefits - Employee Ownership Program - eligible employees share in the financial rewards as the company grows. - Professional Development Opportunities – access to learning and career growth initiatives. - Owner Referral Program. - Work-from-Home Reimbursement. - Digital Health & Wellness Platform - online fitness and wellbeing resources. - Canary Emergency Financial Assistance Program. - Comprehensive Medical, Dental, and Vision Coverage. - Life and AD&D Insurance. - Confidential Employee Assistance Program (EAP). - Health Savings Account (HSA). - Short-Term Disability Coverage. - Voluntary Benefits - supplemental accident, critical illness, and hospital insurance. - Employee Discounts. - 401(k) Plan with company matching contributions. - Paid Time Off Programs. - 11 Company-Paid Holidays.

Role Description IXOPAY is built on three core products that power the full payments lifecycle for enterprise merchants worldwide. Our Payment Orchestration platform routes transactions intelligently across a global network of acquirers and payment methods through a single API. Our Tokenization platform (TokenEx) gives merchants true ownership of their payment data with universal, network, and agnostic tokens that eliminate PSP lock-in and dramatically reduce PCI scope. And our AI Payments Intelligence engine (Congrify) turns fragmented transaction data into actionable insights — optimizing authorization rates, identifying fee-saving opportunities, and powering predictive analytics across the stack. As our Lead AI Developer, you'll be at the center of all of it. This is a greenfield leadership role for someone who wants to own the AI stack, shape the roadmap, and build a team that delivers AI as both an internal accelerant and an external product. What You'll Do: - Architect AI agent systems: - Design, build, and deploy production single-agent and multi-agent systems using LangGraph on cloud AI platforms (Azure AI Foundry, AWS Bedrock, etc.), with Claude as the primary foundation model. - Architect agent memory, state management, and context persistence for long-horizon reasoning tasks. - Own evaluation and quality: - Build and maintain evaluation harnesses that measure faithfulness, hallucination rate, retrieval quality, latency, and instruction-following accuracy across model versions and prompt changes. - Define golden test sets, regression suites, and automated eval pipelines that gate every release. - Build AI safety and guardrails: - Implement safety guardrails, output filtering, and prompt injection defenses across all agent systems. - Lead red-teaming exercises to identify failure modes before they reach production. - Ensure responsible AI practices are embedded in every deployment. - Ship RAG-powered intelligence: - Build agentic RAG pipelines with retrieval, generation, validation, and ReAct-style reasoning loops that ground outputs in real payment data. - Leverage the Claude SDK, Anthropic's tool-use APIs, and MCP protocol to build agents that interact with internal systems, payment gateways, and external data sources. - Manage cost and performance: - Own token cost modeling and inference optimization across agent workflows. - Understand the cost profile of every agent loop at production scale and make architecture decisions that balance capability with spend. - Design human-in-the-loop patterns: - Design human-in-the-loop review checkpoints and escalation paths for high-stakes workflows. - Define where agents operate autonomously and where human oversight is required — especially in payment-critical operations. - Lead delivery streams: - Own the AI delivery streams (Trust Score, StormTrooper, RFC Agent) from ideation through production deployment. - Define technical direction, set quality standards, and make build-vs-buy decisions. - Instrument observability: - Set up and maintain LLM observability and tracing infrastructure (LangSmith, LangFuse, or equivalent) to monitor agent behavior, debug failures, and track quality metrics in production. - Drive AI strategy: - Partner with product, engineering, and payments teams to identify high-impact AI use cases — fraud scoring, intelligent routing, chargeback prediction, automated reconciliation, and beyond. - Build the team: - Mentor contractors and future hires. - Establish coding standards, review patterns, and engineering culture for the AI function. Qualifications - Extensive software development experience, with many years of your career focused on data science, machine learning, and/or agentic AI development (or a combination). - Demonstrated track record of building and deploying production-ready AI agents — not proofs of concept, not notebooks, but systems running in production with real users and real data. - Experience designing and running evaluation frameworks for LLM-powered systems — faithfulness scoring, hallucination detection, retrieval quality metrics, and regression testing across model and prompt changes. - Hands-on experience building agents with LangGraph (state machines, conditional edges, tool nodes, memory management, human-in-the-loop patterns). - Production experience with the Claude SDK / Anthropic API — tool use, MCP protocol, structured outputs, and prompt engineering at scale. - Understanding of AI safety practices: output filtering, guardrail implementation, prompt injection defense, and red-teaming methodologies. - Strong Python skills. You write clean, testable, well-documented code. - Experience with RAG architectures — vector stores (FAISS, Pinecone, OpenSearch), embedding models, chunking strategies, and retrieval evaluation. - Practical understanding of token cost modeling, inference optimization, and the trade-offs between fine-tuning, retrieval-based approaches, and prompt engineering at production scale. - Familiarity with cloud AI platforms (AWS Bedrock, Azure AI Foundry, GCP Vertex AI) and supporting cloud infrastructure services. - Experience with LLM observability and tracing tools (LangSmith, LangFuse, Arize, or equivalent) for monitoring agent behavior and quality metrics in production. - Ability to lead technical decisions and communicate trade-offs clearly to both engineering and business stakeholders. Requirements - Experience in payments, fintech, or financial services. - Background in NLP, information extraction, or document understanding. - Experience with fine-tuning approaches (LoRA, SFT, DPO/RLHF) and the judgment to know when fine-tuning is the right call versus retrieval or better prompting. - Prior experience scaling an AI/ML function from 0 to 1. - Contributions to open-source AI/agent frameworks. Benefits - Competitive salary and benefits. - Opportunities for growth and development. - A collaborative and supportive team environment. - US Benefits Include: - Medical, Dental & Vision Insurance. - Flexible Spending Account (FSA) & Health Savings Account (HSA). - Employer-paid Life, AD&D, STD & LTD Insurance. - Unlimited PTO & Paid Holidays. - 401(k) Plan with Employer Match. Company Description IXOPAY is an Equal Opportunity Employer and provides equal employment opportunities to all employees and applicants without regard to race, color, religion, sex (including pregnancy, sexual orientation, and gender identity), national origin, age, disability, genetic information, veteran status, or any other status protected by applicable federal, state, or local law. IXOPAY participates in E-Verify.

Role Description - Author and review challenging physics problems to train and evaluate frontier AI models. - Evaluate model outputs on tasks like analytical derivation, mechanism prediction, and numerical modeling. - Identify areas where current models fail, especially mistakes a working PhD physicist would catch. - Collaborate with a small program team and other senior contributors. Qualifications - First-author or co-first-author publications in journals like Physical Review Letters, Nature, or Science. - Doctoral or postdoctoral training at a top-20 global institution for physics or STEM. - Competitive fellowships such as NSF GRFP, Hertz, or DOE Computational Science Graduate Fellowship. - Issued patents, spin-out companies, or grants as a named PI or co-PI. - International Physics Olympiad (IPhO) Gold/Silver/Bronze medalist. - Currently or recently in an active research role: PhD candidate, postdoc, instructor, faculty, principal scientist, or industry researcher. Requirements - W-2 employment with Cincinnatus LLC; payroll, benefits, and compliance handled by Cincinnatus. - Compensation reflects research seniority and credentials. - No fixed end date; contributors continue as long as the program needs their expertise. Application Process - Upload resume - AI interview based on your resume - Submit form Resources & Support - For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome - For any help or support, reach out to: support@mercor.com - Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.