Lead Data Scientist

Lead Data Scientist

Remote

Primary Skills:

Devops, html, Azure Functions, Pinecone, develop, Cognitive Services, Azure OpenAI, Azure logic app and Azure Functions for cloud deployment. Develop preprocessing pipelines for document ingestion and transformation across formats (PDF, DOCX, etc.). Optimize prompt engineering and fine-tuning strategies to enhance LLM response relevance and contextuality. Monitor and tune LLM performance using feedback loops, embeddings, and vector databases. Collaborate cross-functionally with front-end developers, and business stakeholders for full solution integration. Required Skills and Qualifications: 4+ years of experience in Data Science or AI Engineering with focus on NLP or LLMs. Hands-on expertise with Python, FastAPI, and Open AI s GPT APIs. Proficient in Azure Cloud, especially Azure Open AI, Azure Cognitive Search, and Blob Storage. Experience building or integrating RAG pipelines with document loaders, chunking strategies, and vector embeddings. Deep understanding of LLM tuning, prompt chaining, and semantic search. Familiarity with Redis, or other vector databases is a plus. Preferred Qualifications: Previous experience in developing, scaling and deploying chatbot solutions in production environments. Experience with MLOps tools for model deployment and lifecycle management. Working knowledge of multilingual LLM optimization and translation APIs (e.g., Azure Translator, Intento). In a. apra The client is seeking an experienced professional with a minimum of 4 years in Data Science or AI Engineering, specializing in NLP and Large Language Models (LLMs). The primary focus of the role is to design, and deploy chatbot solutions using OpenAI GPT-4.0 integrated with custom knowledge sources via RAG (Retrieval-Augmented Generation). The ideal candidate must have hands-on expertise with Python, and Open AI s GPT APIs, and be proficient in Azure Cloud services, including Azure OpenAI, Cognitive Search, and Blob Storage. The role also requires experience building RAG pipelines, optimizing prompt engineering, and monitoring LLM performance using embeddings and vector databases. Collaboration with cross-functional teams is essential, and preferred qualifications include experience with production chatbot deployment, MLOps tools, and multilingual LLM optimization.

Key Responsibilities:

Design, develop, and deploy chatbot solutions using OpenAI GPT-4.0 integrated with custom knowledge sources via RAG.

Build scalable and secure APIs to interface LLMs with front-end and back-end systems.

Implement Azure-native solutions including Azure Cognitive Services, Azure OpenAI, Azure logic app and Azure Functions for cloud deployment.

Develop preprocessing pipelines for document ingestion and transformation across formats (PDF, HTML, DOCX, etc.).

Optimize prompt engineering and fine-tuning strategies to enhance LLM response relevance and contextuality.

Monitor and tune LLM performance using feedback loops, embeddings, and vector databases.

Collaborate cross-functionally with front-end developers, DevOps, and business stakeholders for full solution integration.

Required Skills and Qualifications:

4+ years of experience in Data Science or AI Engineering with focus on NLP or LLMs.

Hands-on expertise with Python, FastAPI, and OpenAI s GPT APIs.

Proficient in Azure Cloud, especially Azure OpenAI, Azure Cognitive Search, Azure Functions, and Blob Storage.

Experience building or integrating RAG pipelines with document loaders, chunking strategies, and vector embeddings.

Deep understanding of LLM tuning, prompt chaining, and semantic search.

Familiarity with Redis, Pinecone, or other vector databases is a plus.

Preferred Qualifications:

Previous experience in developing, scaling and deploying chatbot solutions in production environments.

Experience with MLOps tools for model deployment and lifecycle management.

Working knowledge of multilingual LLM optimization and translation APIs (e.g., Azure Translator, Intento).