AI/ML Data Scientist

We are seeking an experienced AI/ML Data Scientist with strong hands-on experience in Machine Learning, Artificial Intelligence, Generative AI, Large Language Models, NLP, and data science. The ideal candidate will have experience developing end-to-end AI/ML solutions, from data preparation and model development through evaluation, deployment, and optimization.

Strong experience with Python, machine learning algorithms, Generative AI, LLMs, RAG, prompt engineering, vector databases, cloud AI/ML platforms, and MLOps is highly preferred. The candidate should be comfortable translating business requirements into scalable, production-ready AI and data science solutions.

Roles & Responsibilities:

• Design, develop, train, evaluate, and deploy AI/ML models for business and analytical use cases.
• Perform data exploration, statistical analysis, feature engineering, data preparation, and model experimentation.
• Develop machine learning solutions using regression, classification, clustering, forecasting, predictive modeling, and other applicable techniques.
• Build Generative AI and LLM-based applications for enterprise use cases.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines using embeddings, semantic search, vector databases, and document processing.
• Develop and optimize prompt engineering and LLM workflows for intelligent applications.
• Develop NLP solutions involving text classification, semantic similarity, embeddings, document understanding, and natural language processing.
• Work with Agentic AI, AI agents, and intelligent workflow automation where applicable.
• Use frameworks and libraries such as LangChain, LangGraph, LlamaIndex, Hugging Face, Scikit-learn, TensorFlow, and PyTorch.
• Develop scalable data processing and machine learning pipelines using Python, PySpark, Spark, and SQL.
• Work with vector databases and search technologies such as FAISS, Pinecone, pgvector, or equivalent platforms.
• Develop AI/ML APIs and services using FastAPI, Flask, or similar technologies.
• Work with cloud-based AI/ML platforms including AWS, Azure, and Google Cloud Platform.
• Utilize services such as AWS SageMaker, Amazon Bedrock, Azure OpenAI, Azure ML, and Google Vertex AI for AI/ML development and deployment.
• Implement MLOps practices including model versioning, deployment, monitoring, testing, and performance optimization.
• Evaluate model and LLM performance and improve accuracy, relevance, scalability, latency, and reliability.
• Work with structured and unstructured datasets and develop data-driven solutions at scale.
• Collaborate with data engineers, software engineers, architects, product teams, and business stakeholders.
• Translate business requirements into effective AI/ML and data science solutions.
• Present technical findings, model results, and recommendations to both technical and non-technical stakeholders.
• Develop reusable AI/ML components, technical documentation, and best practices.