Generative AI Developer

Generative AI DeveloperWe are seeking a Generative AI Developer to design, develop, and deploy AI-powered applications using large language models (LLMs), multimodal AI models, and modern AI frameworks. The ideal candidate will have experience building production-ready generative AI solutions, integrating AI services into enterprise applications, and optimizing model performance, reliability, and security. This role requires strong software engineering skills, expertise in AI frameworks, and the ability to collaborate with cross-functional teams to deliver innovative AI solutions.Key ResponsibilitiesDesign, develop, and deploy generative AI applications using LLMs and multimodal AI models.Build AI-powered chatbots, virtual assistants, copilots, document intelligence solutions, and content generation applications.Develop Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise knowledge sources.Design and optimize prompts, system instructions, and workflows to improve model performance and accuracy.Integrate foundation models through APIs and cloud AI platforms into web, mobile, and enterprise applications.Fine-tune, evaluate, and optimize AI models where appropriate using modern training and evaluation techniques.Develop AI agents capable of planning, reasoning, and orchestrating multi-step tasks.Implement AI safety measures, content filtering, guardrails, and responsible AI practices.Collaborate with data engineers, software developers, DevOps engineers, product managers, and business stakeholders.Monitor AI application performance, latency, quality, and cost, and continuously improve deployed solutions.Create technical documentation, reusable components, and best practices for AI application development.Stay current with advancements in generative AI, foundation models, agentic AI, and emerging frameworks.Required QualificationsBachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.3–8+ years of software development experience, including hands-on experience with generative AI technologies.Strong proficiency in Python and experience with modern software development practices.Experience working with large language models (LLMs) and foundation model APIs.Hands-on experience with prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, and vector databases.Experience developing REST APIs and integrating AI services into enterprise applications.Knowledge of machine learning fundamentals, NLP, and model evaluation techniques.Familiarity with cloud platforms and AI services.Strong analytical, debugging, and problem-solving skills.Preferred QualificationsExperience building production-scale AI applications.Experience with agentic AI architectures and workflow orchestration.Knowledge of model fine-tuning, parameter-efficient tuning, or reinforcement learning concepts.Experience with multimodal AI applications involving text, images, audio, or video.Experience with MLOps, CI/CD pipelines, and containerized deployments.Contributions to open-source AI projects or published AI research.Relevant cloud or AI certifications.Technical SkillsPythonSQLLarge Language Models (LLMs)Prompt EngineeringRetrieval-Augmented Generation (RAG)AI AgentsLangChainLlamaIndexSemantic KernelHugging Face TransformersOpenAI APIAnthropic APIGoogle Gemini APIEmbeddingsVector Databases (Pinecone, Weaviate, Chroma, FAISS, Milvus)TensorFlow or PyTorchFastAPI or FlaskREST APIsDockerKubernetesGitCI/CDAzure AI, AWS Bedrock, or Google Vertex AIRedisElasticsearchSoft SkillsProblem-solvingCollaborationCommunicationAnalytical thinkingInnovationAdaptabilityTime managementDocumentation skillsContinuous learningKey DeliverablesProduction-ready generative AI applicationsAI chatbots and virtual assistantsRAG pipelines and enterprise search solutionsAI agents and workflow automationPrompt libraries and reusable AI componentsAPI integrations and microservicesTechnical documentationPerformance monitoring dashboardsAI evaluation and testing reportsSuccess MetricsSuccessful deployment of production AI solutionsModel response quality, accuracy, and relevanceReduced latency and infrastructure costsUser adoption and satisfactionReliability and availability of AI servicesCompliance with security and responsible AI standardsOn-time delivery of AI features and enhancementsContinuous improvement based on evaluation metrics and user feedback