About this role
Job Description We are looking for a Senior Architect, Machine Learning to define and lead the architecture for enterprise-grade Generative AI and Agentic AI systems. This is a senior, hands-on architecture role focused on building reliable, scalable, secure, and cost-efficient AI platforms - covering RAG, agent orchestration, inference infrastructure, evaluation/guardrails, and production operations across multiple tenants. You will work at the intersection of research innovation and engineering reliability: enabling rapid experimentation while ensuring the system runs 24/7 with strong SLOs, governance, and predictable cost. Responsibilities Architecture & Technical Leadership Own the end-to-end architecture for RAG + agentic workflows (Plan → Execute → Verify) across enterprise use cases (contracts, PDFs, knowledge bases). Define architecture standards for multi-tenant isolation, API design, service boundaries, and integration patterns. Lead technical decision-making: build vs buy, model strategy (hosted vs open-weights), tooling selection, and performance/cost tradeoffs. Drive architecture reviews, mentor engineers/researchers, and raise the overall bar for engineering quality and research rigor. RAG & Retrieval Systems (Enterprise-grade) Design retrieval pipelines that optimize grounded accuracy: chunking strategy, hybrid retrieval, reranking, query rewriting, and context construction. Define document ingestion patterns (PDF parsing, OCR, structured extraction, metadata enrichment) and index lifecycle strategies. Establish retrieval evaluation and regression frameworks (ground truth, offline/online evaluation, drift tracking). Enable async and event-driven architectures for long-running tasks using queues/streams (Kafka/RabbitMQ/Redis Streams) and/or durable workflow engines (Temporal). Inference & Platform Engineering Architect model serving for high throughput and low latency using engines like vLLM / TGI / Triton / TorchServe (as applicable). Define GPU orchestration and capacity strategy on Kubernetes (AKS/EKS/GKE), including scale-to-zero, scheduling, and quota-based governance. Design platform-level controls for rate limiting, caching, backpressure, and cost containment (tenant quotas, token budgets, throttling). Safety, Guardrails, Security & Compliance Own guardrail architecture for prompt injection defense, tool safety, policy enforcement, and PII handling (redaction patterns). Define secure-by-default patterns: secrets management, data protection, audit logs, and safe prompt/tool execution boundaries. Partner with security/compliance teams to meet enterprise standards (e.g., SOC2/GDPR expectations where relevant). Observability, Reliability & Operational Excellence Establish SLOs and production readiness standards: error budgets, runbooks, incident response patterns. Define observability strategy across LLM calls and agent tools: tracing, metrics, logs, cost dashboards, and token usage reporting. Build reliability patterns for dependency failure (model provider downtime, throttling): circuit breakers, fallbacks, degradation strategies.