- Design and develop enterprise-grade Generative AI solutions using Python and up-to-date AI frameworks.
- Build AI applications using LLMs, embeddings, prompts, function calling, tool usage, and agentic AI patterns.
- Implement Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge retrieval and generation use cases.
- Design end-to-end AI solutions covering data ingestion, data processing, retrieval, generation, evaluation.
- Work with Agentic AI frameworks such as CrewAI, LangChain, LangGraph, LlamaIndex, AutoGen, and Langfuse.
- Apply prompt engineering strategies to improve LLM accuracy, reliability, and usability.
- Evaluate the suitability of fine-tuning vs RAG based on business and technical requirements.
- Support LLM fine-tuning workflows including instruction tuning and supervised fine-tuning.
- Manage data preparation for fine-tuning, including train/validation splits, data quality checks, and contamination prevention.
- Deploy AI workloads on cloud platforms such as Azure, AWS, or GCP.
- Containerize AI applications using Docker and work with basic Kubernetes concepts.
- Apply model engineering best practices for scalable, reliable, and production-ready AI systems.
Preferred Skills:
- Technology->OpenSystem->Python - OpenSystem->Python
- Technology->AI-AI Engineering->AI/ML Solution Architecture and Design->traditional ai ml