Technical Architect - AI & Innovation

About the Role

The Technical Architect - AI & Innovation is responsible for creating a robust technical foundation that accelerates Azentio's Forward Deployed AI and product teams by eliminating the need to rebuild core infrastructure repeatedly. This role owns the architecture that spans model access, orchestration, APIs, context and knowledge management, evaluation, observability, security, and deployment. The position requires close collaboration with real customer implementations to ensure the architecture aligns with production needs. The goal is to strike a balance between rapid experimentation and delivering secure, reusable, enterprise-grade solutions.

Core Responsibilities

• Develop enterprise-level architectural patterns tailored for AI applications and agentic systems.
• Design standardized methods for model access, routing, and integration that are neutral to providers.
• Create reusable components for agent runtime, orchestration, tools, and APIs.
• Define common frameworks for context management, knowledge handling, retrieval processes, memory, and data access.
• Implement evaluation, tracing, observability, and cost monitoring for AI workloads.
• Partner closely with Forward Deployed Engineering teams on active deployments, capturing recurring patterns for reusable solutions.
• Offer architectural guidance that supports ongoing experimentation without causing delay.
• Develop reference architectures and accelerators to streamline future deployments.
• Integrate insights from field experiences back into the common platform and product architectures.
• Set standards for API patterns, event handling, identity, authorization, data access, and enterprise system integration.
• Guide deployment strategies across cloud, private, and hybrid environments to support AI workloads.
• Define scalable model serving, gateway, secrets management, networking, and runtime isolation approaches.
• Advise build-vs-buy decisions regarding models, platforms, frameworks, and infrastructure components.
• Establish technical controls governing agent authority, tool permissions, human approvals, and auditing.
• Shape security, privacy, model governance, and responsible AI architecture.
• Set production standards ensuring reliability, fallback strategies, observability, incident response, and lifecycle management.
• Maintain flexibility in model and framework choices to avoid vendor lock-in.
• Lead architecture reviews for critical AI projects and deployments.
• Mentor Lead and Senior Forward Deployed Engineers on architectural and engineering trade-offs.
• Keep a pragmatic architecture roadmap grounded in product and field learnings rather than trends.
• Challenge unnecessary complexity and prevent both fragmented experimentation and bureaucratic architecture processes.

Required Technical Competencies

• Comprehensive knowledge of large language model (LLM) architecture, retrieval augmented generation (RAG), tool usage, agent orchestration, evaluation methods, and model lifecycle management.
• Expertise in model gateways, routing, hosting models (both cloud and open-source), and inference trade-offs.
• Ability to critically evaluate agent frameworks and patterns without dependency on any single platform.
• Solid architecture experience with distributed systems, microservices, APIs, and event-driven designs.
• Proficient software engineering skills in Python, Java, and enterprise-grade technology stacks.
• Capable of reviewing designs at both code and system levels to identify cross-layer architectural challenges.
• Deep understanding of relational database systems, enterprise data design, and data integration strategies.
• Expertise in APIs, messaging systems, identity management, authorization mechanisms, and agent tool interfaces.
• Extensive hands-on experience with cloud platforms such as Azure, AWS, or GCP, including cloud-native architectural patterns.
• Strong familiarity with containerization (Docker), orchestration (Kubernetes), CI/CD pipelines, observability tools, networking, security, and production operations.
• Experienced in establishing enterprise-grade governance, security, auditability, privacy, and responsible AI controls.
• Ability to architect systems supporting human approval workflows, bounded agent authorities, and full action tracing.

Preferred Qualifications

• Background in AI platform architecture for enterprise products.
• Experience with Banking, Financial Services, Insurance (BFSI), or ERP product architecture.
• Proven track record deploying AI solutions at large scale and in production environments.
• Previous involvement designing reusable platform capabilities based on recurrent customer or product implementation patterns.

Eligibility

Applicants should hold a B.Tech or B.E. degree in any specialization.