OvalEdge
Website:
ovaledge.com
Job details:
About OvalEdgeOvalEdge is an enterprise-grade AI-powered Data Intelligence platform that unifies Data Catalog, Data Governance, Data Quality, and Analytics into a single, AI-native SaaS product. Trusted by Fortune 500 companies globally, OvalEdge enables organizations to discover, govern, and operationalize their data assets at scale — accelerating data-driven decisions while ensuring compliance and trust.
The RoleWe are looking for a Senior Technical Architect to serve as the primary hands-on technical owner for OvalEdge's AI Product Platform. This is a deeply hands-on role — you will own the end-to-end technical architecture for a multi-tenant SaaS platform spanning Generative AI, Agentic AI, RAG, Data Governance, Cloud-native infrastructure, and Enterprise Integrations, working shoulder-to-shoulder with the engineering team that builds it.
You have done this before. You have shipped AI capabilities to production — not just designed them on whiteboards. You are equally comfortable presenting architecture strategy to the C-suite and doing a deep-dive code review with an engineering team. You thrive in a fast-moving product company where architecture decisions have direct customer impact.
What You'll DoTechnical Architecture & Design Leadership- Own the technical architecture and drive a rolling 24-month technology roadmap aligned to product and business objectives.
- Define and drive adoption of architecture standards, design principles, and decision records across engineering — leading by example through hands-on design work, not policy alone.
- Drive technical design reviews for all major platform initiatives and own the final call on system design trade-offs.
- Identify and proactively retire technical debt; ensure the platform stays extensible for emerging AI technologies.
AI & Agentic Platform- Design and evolve production-grade Agentic AI systems — multi-agent orchestration, hierarchical supervisor-agent frameworks, goal-oriented task decomposition, and long-running autonomous workflows.
- Define standards for Generative AI integration: multi-LLM routing, model abstraction layers, prompt and context engineering, token management, and cost optimization across OpenAI, Anthropic, Gemini, and open-source models.
- Architect RAG pipelines end-to-end: vector databases, embedding strategies, retrieval optimization, hallucination mitigation, and evaluation frameworks.
- Ensure Responsible AI practices — AI security, data privacy, bias controls, and governance compliance — are baked into every AI system design.
- Drive LLMOps maturity: model versioning, observability, drift detection, and automated evaluation in production.
SaaS & Cloud Platform- Own the architecture for OvalEdge's multi-tenant SaaS platform on AWS — ECS/EKS, Lambda, S3, RDS, OpenSearch, Bedrock, IAM, and CloudWatch.
- Design for elastic scaling, disaster recovery, cost efficiency, and 99.9%+ availability SLAs.
- Establish self-healing, self-monitoring, and auto-remediation capabilities; drive observability and reliability engineering maturity.
- Define containerization, CI/CD, and Infrastructure-as-Code standards across engineering teams.
Data & Analytics Architecture- Design scalable data catalog, governance, and analytics architectures — semantic data layers, query optimization, in-memory analytics, and AI-assisted analysis pipelines.
- Architect MCP-based enterprise integration services: tool discovery, agent interoperability, REST and event-driven APIs, and partner SDK frameworks.
Engineering Excellence & Technical Mentorship- Provide hands-on technical mentorship to AI Engineers, Platform Engineers, and fellow Architects — through pairing, code review, and design-review shadowing, not just top-down direction.
- Drive AI-assisted development, code generation tooling, and developer productivity improvements across the SDLC.
- Partner with QA leadership on test strategy — including AI-assisted testing, performance, security, and reliability testing.
- Work closely with Product Management to assess feasibility, define solution approaches, and shape the product roadmap.
- Communicate technical strategy and trade-offs clearly to executive stakeholders, translating architectural decisions into business impact.
What You'll BringRequired- 15+ years in software engineering and architecture; 8+ years as a senior/staff/principal-level hands-on technical architect at a software product company.
- Hands-on, demonstrated experience shipping AI/ML capabilities to production.
- 5+ years building and scaling multi-tenant SaaS products on cloud-native platforms (AWS preferred).
- 5+ years building and scaling licensed, on-prem products on multiple platforms.
- Experience in managing architecture for products deployed across SaaS and multiple, on-prem platforms.
- Deep expertise in Agentic AI frameworks (LangChain, LangGraph, CrewAI, AutoGen, MCP) and RAG architectures in production.
- Strong command of Generative AI ecosystem: LLM providers (OpenAI, Anthropic, Gemini), model abstraction, prompt engineering, and LLMOps.
- Expert-level proficiency in Python and Java; solid understanding of microservices, REST APIs, and event-driven systems.
- Demonstrated track record driving technical direction and design decisions across cross-functional engineering teams through large-scale platform builds.
- Strong foundation in software architecture and system design fundamentals — distributed systems, domain-driven design, API design, and scalability patterns.
Nice to Have- 3+ years designing and shipping Generative AI or Agentic AI products commercially.
- Experience with Data Catalog, Data Governance, Data Quality, or Analytics platforms.
- Exposure to vector databases (pgvector, Pinecone, Weaviate, OpenSearch) and semantic search in production.
- Experience with AWS Bedrock, SageMaker, or similar managed AI/ML services.
- Background supporting enterprise-scale, Fortune 500 customers.
- Master's degree in Computer Science, AI/ML, or a related field.
Education- Bachelor's degree in Computer Science, Engineering, or a related field (required).
- Master's degree or AI/ML specialization (preferred).
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