Machine Learning Engineer

Key Responsibilities

• Design, build, and deploy production-grade AI/ML solutions end-to-end.
• Develop scalable ML and Generative AI 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 frameworks like Hugging Face, LangChain, and LlamaIndex.
• Implement and integrate vector database–based 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 such as 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 the model lifecycle.
• Experience with Generative AI, LLMs, embeddings, vector databases, and RAG architectures.
• Hands-on experience developing APIs and deploying ML services.
• Cloud experience with at least one major provider (AWS, GCP, or Azure).