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Job Title: Lead AI Engineer Location: Hyderabad About the Role: We are seeking an experienced Lead AI Engineer to architect, develop, and scale our enterprise AI infrastructure and content processing pipelines. You will lead the technical design of AI systems, focusing on Retrieval-Augmented Generation (RAG) frameworks, LLM gateways, and intelligent agentic workflows. As a technical leader, you will ensure our AI data pipelines are robust, highly structured, and seamlessly integrated into our AWS cloud ecosystem. In this role you will... AI Architecture Strategy: Lead the architectural oversight and technical direction for content ingestion and enrichment pipelines, ensuring scalable and efficient AI integrations. RAG LLM Integration: Design, deploy, and optimize advanced Retrieval-Augmented Generation (RAG) frameworks and centralized LLM gateways. Agentic Workflows: Build and orchestrate multi-agent platforms and workflows using modern AI integration frameworks. Protocol Implementation: Drive the implementation of Model Context Protocol (MCP) to standardize and streamline interactions between foundation models and enterprise backend systems. Data Pipeline Engineering: Architect highly reliable LLM generation pipelines, enforcing strict structured JSON outputs to guarantee schema validation and application parsing stability. Technical Leadership System Design: Mentor engineering teams, apply best-in-class software design patterns, and guide infrastructure deployment planning across cloud environments. You've got what it takes if you have… Experience: 5-8 years of professional software engineering experience, with a proven track record of designing and deploying enterprise-grade systems and AI platforms. Programming Frameworks (Mandatory): Deep, hands-on expertise in Java and the Spring / Spring Boot framework. Good working knowledge of Python is also required for AI/ML integrations and scripting. Database Expertise (Mandatory): Strong knowledge of both relational databases (RDBMS such as PostgreSQL or MySQL) and NoSQL databases, specifically MongoDB. Design Patterns (Mandatory): Extensive knowledge of object-oriented design, architectural design patterns, and enterprise integration patterns for building scalable, decoupled systems. Cloud Infrastructure (Mandatory): Hands-on experience with AWS cloud services, including the deployment, scaling, and management of containerized applications and data pipelines. AI/ML Expertise: Practical experience building scalable RAG applications, vector search optimizations, and LLM orchestration layers. Data Engineering: Proven ability to build resilient data ingestion workflows with a strong emphasis on JSON-based structured data validation over legacy text formats. Good to have skills... Familiarity with enterprise AI toolkits (such as Google ADK). Experience managing regional infrastructure rollouts and navigating model availability constraints across different deployment stages. #LI-Onsite