- 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
- Primary skills Technology Data Science Machine Learning Technology Geographical Information System Spatial Databases SQL Server Technology Machine Learning Python
- 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
Technology->AI-Data science->Machine Learning,Technology->AI-Data science->PYTHON,Technology->AI-Generative AI->Generative AI - Basic->retrieval augmented generation (rag)
