Senior Technical Architect

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.
Location: Hyderabad Reports To: SVP Engineering Experience: 15+ years (5+ in AI/ML)
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).

Work arrangement
No