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.
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