Principal AI Engineer at InvoiceCloud

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

05 Aug, 26

Salary

0.0

Posted On

07 May, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Platform Architecture, Multi-agent Orchestration, Azure ML, Python, .NET, LLM Systems, RAG, System Design, Kubernetes, CI/CD, Prompt Management, AI Governance, FinOps, API Gateway, Distributed Systems, Cloud-native Platforms

Industry

Financial Services

Description

About InvoiceCloud: InvoiceCloud is a fast-growing fintech leader recognized with 20 major awards in 2025, including USA TODAY and Boston Globe Top Workplaces, multiple SaaS Awards wins for Best Solution for Finance and FinTech, and national customer service honors from Stevie and the Business Intelligence Group. Judges also highlighted our mission to reduce digital exclusion and restore simplicity and dignity to how people pay for essential services, as well as our leadership in AI maturity and responsible innovation. It’s an award-winning, purpose-driven environment where top talent thrives. To learn more, visit InvoiceCloud.com. About InvoiceCloud InvoiceCloud is the leading digital payments platform for utilities, insurance carriers, municipalities, and financial institutions. We enable billers to present bills digitally and collect payments across every channel — driving digital adoption, reducing costs, and improving the customer experience. With 3,000+ billing organizations, 240M+ invoices, and $38B+ in annual payment volume, we're investing heavily in AI to transform from a best-in-class payments processor into a Billing Intelligence Platform. About the Role We're building a shared AI platform that powers the next generation of our product portfolio — multi-agent orchestration, ML scoring infrastructure, and LLM-powered intelligence products. As Principal AI Engineer, you'll architect this platform, set the technical direction, and define the engineering standards that the organization builds against. This is a hands-on technical leadership role — not a management position. You set architectural direction and write production code every day. You think in ADRs, trade-off matrices, and failure modes. You use Claude Code and AI accelerators to operate at 3–5x speed, and you teach the team to do the same. Your work directly shapes a growing AI product portfolio. The platform you architect becomes the shared foundation — every subsequent product launches faster because of the infrastructure decisions you make now. You'll be based in India, working hybrid (3 days in-office per week), and will report to the VP of Engineering / CTO. What You'll Do Own AI Platform Architecture : Design the full AI platform — multi-agent orchestration, ML inference infrastructure, LLM gateway, tool registry, and eval/observability stack Write Architecture Decision Records (ADRs) that the organization builds against Make build-vs-buy decisions, evaluate emerging frameworks (A2A, MCP, Visa TAP), and define integration patterns Design system-level concerns: auth gating, policy enforcement (OPA), API gateway patterns (Kong), and multi-tenant isolation Define reference architectures for AI-enabled services across .NET 10 APIs, Python ML workloads, React frontends, AKS deployments, Kong gateway integration, and Azure DevOps delivery pipelines Drive Product AI Strategy : Partner with Product and CTO office to translate business objectives into AI technical strategy Identify where AI creates differentiated value — predictive models, conversational agents, intelligent automation Evaluate and de-risk emerging technologies. Present to executive leadership on AI capabilities, costs, and competitive positioning Define the AI FinOps strategy: token metering, model routing for cost optimization, quota management, and budget forecasting Establish clear platform decision rules for when AI capabilities should be implemented as .NET services, Python services, Azure ML pipelines, background jobs, or embedded product workflows AI Safety, Governance and Responsible AI : Define and enforce the Responsible AI framework — content safety guardrails, PII governance, bias testing, and audit trails Ensure all AI systems meet PCI DSS, SOC 2, and emerging AI regulation requirements Establish model validation and governance processes — transparency, explainability, and fairness are non-negotiable Build the trust infrastructure that enables the organization to deploy AI confidently in regulated financial services Champion ethical AI principles; ensure all agents and models comply with global financial regulations and internal compliance standards Write Code - Everyday : Write production code, review critical PRs, debug complex agent interactions, and prototype new capabilities Lead by example — your code quality, testing discipline, and documentation set the standard Use Claude Code and AI accelerators daily. Build custom skills, MCP integrations, and automation workflows Mentor and Multiply the Team : Raise the AI engineering bar across the organization. Run design reviews, pair on complex problems Champion AI-accelerated development — train the team on Claude Code workflows, custom skills, and agentic tooling Define engineering standards, evaluation criteria, and best practices that scale beyond your direct team Must-Have Qualifications 10+ years in software engineering with 5+ years in AI/ML — you've architected and shipped production AI systems System design mastery — multi-agent architectures, ML platforms, or large-scale inference systems Azure + Microsoft AI stack deep expertise — Azure ML, AI Foundry, Semantic Kernel, .NET agent frameworks Python + .NET bilingual — Python for ML/data, .NET/C# for agent runtimes LLM systems at production scale — prompt management, RAG, content safety, agentic workflows AI-first development velocity — Claude Code, Cursor, or equivalent at 3–5x speed Deep experience architecting modern .NET cloud-native platforms — including API-first service design, distributed systems, Kubernetes deployment, gateway integration, and CI/CD governance Nice-to-Have FinTech / Payments architecture — PCI DSS, SOC 2, regulated AI Agentic Commerce & A2A — Google A2A, MCP, Visa TAP Technical leadership track record — mentored teams, ADR processes, C-level presentations Omnichannel & voice AI — web chat, voice, SMS, Azure Communication Services ML platform engineering — model registries, feature stores, drift detection Published or open-source contributions in AI/ML Education Bachelor's degree in Computer Science, AI/ML, Mathematics, or a related technical field required. A Master's degree is preferred — but we value what you've built and the systems you've architected over credentials. InvoiceCloud is committed to providing equal employment opportunities to all employees and applicants. We do not tolerate discrimination or harassment of any kind based on race, color, religion, age, sex, nationality, disability, genetic information, veteran or military status, sexual orientation, gender identity or expression, or any other characteristic protected under applicable laws. This commitment applies to all aspects of employment, including recruitment, hiring, placement, promotion, termination, layoff, recall, transfer, leave, compensation, and training. If you require a disability-related or religious accommodation during the application or recruitment process, and wish to discuss possible adjustments, please contact jobs@invoicecloud.com. Click here to review InvoiceCloud’s Job Applicant Privacy Policy. For recruitment agencies: InvoiceCloud does not accept unsolicited resumes from agencies. Please do not forward resumes to our job aliases, employees, or any other company location. InvoiceCloud is not responsible for any fees associated with unsolicited submissions.

Responsibilities

Architect and build a shared AI platform featuring multi-agent orchestration and ML scoring infrastructure to power a Billing Intelligence Platform. Lead technical direction through Architecture Decision Records while writing production code and mentoring the engineering team.