Reports To: SVP Engineering

Experience:
15+ years (5+ in AI/ML) About OvalEdge OvalEdge 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 Role We 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 Do
Technical 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 Bring Required
• 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). What Success Looks Like In your first 90 days, you will have assessed the current architecture, identified the top 3 risks, and delivered an initial 12-month roadmap. Within 12 months, the platform will demonstrate measurable improvements across these dimensions: Area: Platform Reliability Metric: Uptime Target: > 99.9% Area: AI Quality Metric: Agent success rate & RAG accuracy Target: Improving QoQ Area: Engineering Delivery Metric: Roadmap predictability Target: ≥ 90% Area: Scalability Metric: Customer growth support w/o redesign Target: × headroom Area: Cost Efficiency Metric: AI & infra cost per transaction Target: Optimized YoY Why OvalEdge?
• Build at the intersection of AI and Data — one of the highest-impact domains in enterprise software.
• Greenfield technical architecture ownership with real production scale and Fortune 500 customer exposure.
• Collaborative, engineering-first culture with a direct line to product strategy and executive leadership.
• Competitive compensation, equity participation, and versatile remote-first work environment.

📌 Senior Technical Architect (Hyderabad)
🏢 OvalEdge
📍 Hyderabad