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Strong global brand, metro Hyderabad location, and broad full-stack plus GenAI requirements increase qualified applicant density.
Advanced GenAI, MLOps and agentic-system expertise is transferable but favors candidates with specialized LLM experience.
Explicit 7+ years, required GenAI/LLM and MLOps expertise, and specific tooling create rigid shortlisting filters.
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Build and deploy end-to-end agentic AI and classical ML systems including advanced multimodal generative AI applications, ensuring production-readiness and monitoring.
Design scalable AI workflows with Large Foundational Multimodal Models, implement Model Context Protocol servers/clients, and lead agentic system development including state machines and conversational search systems.
Manage full-stack development cycle leveraging AI coding agents; responsibilities include cloud-native architecture on AWS/multicloud, DevOps/MLOps lifecycle, containerization, CI/CD, and performance optimization.
7+ years experience in AI/ML engineering and Data Science; 3+ years hands-on with generative LLMs and AI agents.
Strong production-grade Python programming skills; TypeScript proficiency is advantageous but not mandatory.
Experience with LangChain or equivalent LLM orchestration frameworks, AWS cloud design including GenAI services, vector and graph databases, and software engineering best practices (API design, Docker, CI/CD).
Bachelor's degree or higher in Computer Science, Physics, Statistics, Mathematics, or related field; English proficiency at C1+ level; occasional work across Central European Timezone.
Experienced full-stack AI engineer combining deep expertise in agentic systems, generative AI, and classical ML with end-to-end ownership from concept to production and monitoring.
Comfortable working in agile, collaborative environments using AI coding agents and advanced cloud-native technologies to accelerate delivery and optimize performance.
Skilled in consulting with stakeholders on AI technology applications with strong awareness of AI ethics including bias mitigation and responsible deployment.