Senior GenAI Engineer

Key Responsibilities:AI System Design & Development: Architect, develop, and deploy large-scale Generative AI, LLM-based systems, including intelligent agents and automation workflows.LLM Integration & Optimization:Integrate and optimize large language models for reasoning, summarization, and structured content generation.Apply prompt design, fine-tuning, and evaluation strategies to ensure reliable and domain-aware outputs.Knowledge & Retrieval Systems:Design and implement retrieval-augmented and context-aware AI pipelines, combining embeddings, semantic search, and hybrid retrieval methods.Backend Engineering:Build robust, scalable backend services and APIs using TypeScript and Python, including real-time communication and data streaming capabilities.Ensure high performance, fault tolerance, and clean integration between AI components and backend systems.Data Pipelines & Processing:Develop and manage pipelines to extract, process, and transform unstructured data (code, documents, text) into AI-ready formats.Infrastructure & Deployment:Design and maintain cloud-native, containerized, and event-driven architectures with Infrastructure-as-Code (IaC) practices.Collaborate with DevOps teams to implement CI/CD, observability, and environment automation.Model Evaluation & Monitoring:Establish model evaluation metrics, continuous validation workflows, and performance dashboards to ensure production reliability and drift detection.Leadership & Mentorship:Lead design discussions, perform technical reviews, and mentor AI engineers.Collaborate cross-functionally with product and platform teams to translate AI capabilities into production-grade solutions. Required SkillsStrong proficiency in Python and TypeScript, with a solid background in backend or API development.Proven experience in LLM-based application design, Generative AI workflows, or AI agent systems.Understanding of retrieval-augmented generation (RAG) and semantic/embedding-based search principles.Experience building scalable cloud-native services, including event-driven or asynchronous architectures.Familiarity with infrastructure automation, container orchestration, and CI/CD pipelines.Exposure to MLOps principles — model lifecycle management, evaluation, and continuous improvement.Strong problem-solving, analytical, and architectural reasoning skills.Excellent communication, collaboration, and mentoring abilities.Preferred Qualifications & Experiences:Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or related disciplines.3+ years of professional experience in AI/ML software engineering, including 2+ years in Generative AI or LLM-based systems.Previous experience leading small engineering teams or driving architectural decisions is highly preferred.