Python-GenAI-LLMOps Professional

Job description

Educational Requirements Bachelor of Engineering,BTech,BCA,BSc,MTech,MSc,MCA Service Line Data Analytics Unit Responsibilities - Key Responsibilities: Develop and deploy Generative AI applications using LLMs (GPT, Llama, etc.) - Build and maintain LLM pipelines, including prompt engineering and fine-tuning - Design and implement end-to-end AI/ML workflows (data model deployment) - Work with vector databases for semantic search and retrieval (RAG architecture) - Implement LLMOps practices for monitoring, evaluation, and versioning - Integrate LLMs into applications via APIs and microservices - Optimize model performance, cost, and latency - Collaborate with data engineers, product teams, and stakeholders - Conduct testing, validation, and debugging of AI models - Ensure security, compliance, and responsible AI practices Additional Responsibilities: Preferred Skills (Nice-to-Have) - Experience with fine-tuning LLMs / parameter-efficient tuning (LoRA, PEFT) - Knowledge of multimodal AI (text, image, audio) - Exposure to cloud platforms (AWS, Azure, GCP) - Experience with Kubernetes and scalable deployments - Knowledge of data engineering tools (Spark, Kafka) - Familiarity with vector search optimization and embeddings - Understanding of AI governance, ethics, and compliance - Experience with chatbots, copilots, or conversational AI systems Technical and Professional Requirements: - Primary skills:Technology->Data Science->Machine Learning,Technology->Geographical Information System->Spatial Databases->SQL Server,Technology->Machine Learning->Python Preferred Skills: - Technology->AI-Data science->PYTHON - Technology->AI-Data science->Machine Learning - Technology->AI-Generative AI->Generative AI - Basic->retrieval augmented generation (rag)