Associate AI Solution Engineer

Purpose

To design, develop, deploy, and support enterprise AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), intelligent chatbots, and AI agent frameworks. The role will focus on building secure, scalable, and business-ready AI applications for internal users and business functions including Research & Development, IT, Regulatory Affairs, Clinical Operations, Quality Assurance and corporate functions.

Responsibilities

• AI Chatbot, RAG & Agent Development:
• Develop research and product roadmaps aligning with scientific and business objectives.
• Develop AI-powered chatbots and virtual assistants for internal business users.
• Build RAG-based applications that retrieve answers from SharePoint, PDFs, Word documents, SOPs, policies, databases, and internal knowledge repositories.
• Create AI agent workflows for knowledge retrieval, document review, authoring, and business process automation.
• Use frameworks such as Lang Chain, Lang Graph, Llama Index, Semantic Kernel, or similar tools under technical guidance.
• Apply prompt engineering, grounding techniques, response validation, and evaluation methods to improve answer quality and reduce hallucinations.

Data & Knowledge Integration

• Support document ingestion pipelines for PDFs, Word files, Excel files, SOPs, regulatory content, and knowledge articles.
• Work on chunking, metadata extraction, embeddings, vector indexing, semantic search, and citation-based response generation.
• Use vector databases/search tools such as Azure AI Search, Azure Cosmos DB

Application Development & Deployment

• Develop backend APIs and AI services using Python, Fast API, Flask, or similar frameworks.
• Develop document processing solutions using OCR, Natural Language Processing (NLP), and intelligent data extraction technique.
• Support deployment and maintenance of AI applications using Azure services, Docker, Git, and CI/CD pipelines.

Security, Governance & Documentation

• Follow organizational security, privacy, access control, and data governance standards while developing AI solutions.
• Support responsible AI practices including auditability, monitoring, explainability, and controlled use of enterprise data.
• Prepare technical documentation, user guides, architecture notes, test cases, and validation-support documents where required.