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 Required Skills & Qualifications Technical Skills Strong proficiency in Python Hands-on experience with Generative AI & LLM frameworks: OpenAI / Azure OpenAI APIs Hugging Face Transformers LangChain / LlamaIndex Experience with prompt engineering and RAG (Retrieval-Augmented Generation) Familiarity with vector databases (FAISS, Pinecone, Weaviate, Chroma) Knowledge of ML/DL frameworks (PyTorch / TensorFlow - basics) Experience in API development (FastAPI, Flask) Understanding of RESTful services and microservices architecture

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

Python-GenAI-LLMOps