Generative AI Engineer

Key Responsibilities:

Design, build, and deploy production-grade AI/ML solutions end-to-end.

Develop scalable ML and GenAI applications using Python and SQL.

Build and fine-tune ML models using libraries such as Scikit-learn, XGBoost, and LightGBM.

Work on NLP/GenAI initiatives leveraging Hugging Face, LangChain, and LlamaIndex.

Implement and integrate vector databasebased solutions for search, retrieval, and RAG pipelines.

Develop and deploy ML/GenAI services using FastAPI or Flask.

Containerize and deploy applications using Docker and Kubernetes.

Work with cloud-native ML platforms on AWS (SageMaker), GCP (Vertex AI), or Azure ML.

Utilize and integrate Text-to-SQL models such as SQLCoder.

Required Skills:

Strong proficiency in Python and SQL.

Solid understanding of machine learning algorithms and model lifecycle.

Experience with GenAI, LLMs, embeddings, vector databases, and RAG architectures.

Hands-on experience in developing APIs and deploying ML services.

Cloud experience with at least one major provider (AWS/GCP/Azure).