We are seeking a talented Generative AI Developer to design, develop, and deploy AI-powered applications leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and modern AI orchestration frameworks. The ideal candidate will have hands-on experience building AI workflows, developing reusable prompt-based solutions, integrating vector databases, and exposing AI capabilities through scalable APIs and microservices. This role will work closely with AI architects, data engineers, product owners, and business stakeholders to build innovative GenAI applications that solve real-world business problems while ensuring reliability, scalability, and performance. We are seeking a talented Generative AI Developer to design, develop, and deploy AI-powered applications leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and modern AI orchestration frameworks. The ideal candidate will have hands-on experience building AI workflows, developing reusable prompt-based solutions, integrating vector databases, and exposing AI capabilities through scalable APIs and microservices. This role will work closely with AI architects, data engineers, product owners, and business stakeholders to build innovative GenAI applications that solve real-world business problems while ensuring reliability, scalability, and performance. ### Responsibilities AI Application Development - Develop and deploy GenAI applications using leading LLM platforms and APIs. - Create intelligent workflows and AI-powered business solutions leveraging modern orchestration frameworks. - Translate business requirements into scalable AI features and reusable software components. - Participate in the full development lifecycle from prototyping through production deployment. Prompt Engineering & LLM Integration - Design, test, and optimize prompts for consistent and high-quality LLM outputs. - Develop reusable prompt templates and structured response frameworks. - Implement techniques for output control, response validation, and prompt optimization. - Support prompt testing and tuning to improve model performance and user experience. RAG & Knowledge Retrieval Solutions - Develop Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge applications. - Integrate vector databases, embeddings, and semantic search capabilities. - Build document ingestion, indexing, chunking, and retrieval workflows. - Optimize retrieval quality, relevance, and response accuracy. API & Microservices Development - Design and build RESTful APIs and microservices to expose AI capabilities. - Integrate GenAI solutions with enterprise applications, databases, and third-party platforms. - Develop scalable backend services supporting AI-driven use cases. - Ensure security, maintainability, and performance of deployed AI services. Testing & Quality Assurance - Develop testing strategies for prompts, workflows, and AI-generated outputs. - Utilize synthetic datasets to validate AI use cases and edge cases. - Monitor solution accuracy, reliability, and business performance metrics. - Support evaluation and benchmarking activities for AI applications. Collaboration & Innovation - Work closely with AI Architects and Senior GenAI Engineers on enterprise AI initiatives. - Participate in AI accelerators, reusable framework development, and innovation programs. - Stay current with developments in LLMs, NLP, prompt engineering, and agentic AI technologies. - Contribute to AI best practices, coding standards, and knowledge-sharing initiatives. ### Qualifications - Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field. - 3–6 years of software development experience with at least 1–2 years focused on Generative AI solutions. - Strong foundation in Large Language Models (LLMs), NLP, prompt engineering, and AI application development. - Proficiency in Python and AI development frameworks. - Experience using LangChain or similar frameworks for workflow orchestration. - Experience developing prompt templates, structured outputs, and reusable AI components. - Hands-on experience building APIs and microservices for AI-driven applications. - Experience with embeddings, vector databases, and Retrieval-Augmented Generation (RAG) architectures. - Familiarity with synthetic data generation and AI testing methodologies. - Strong problem-solving and debugging skills.