Position Overview: We seek a skilled and innovative AI Engineer with background experience in Python, LangChain, AI, ML, and Data Science principles to design, develop, and deploy Agentic AI Agents / Vertical LLM Agents. The ideal candidate will possess extensive experience with LangChain, data science workflows, prompt engineering, retrieval-augmented generation (RAG), and LLM fine-tuning. You will be working to integrate structured and unstructured data into scalable knowledge bases and evaluate systems for continuous improvement. The role involves developing solutions for use cases primarily for UK-based clients and solving industry-specific challenges with cutting-edge AI technologies. Job Type: Full-Time Location: Powai, Mumbai Salary Range: Competitive, based on experience Working Hours: 10:30 am to 7:30 pm Indian Standard Time Days of work: Monday to Friday Key Responsibilities: 1. Knowledge Base Development and Integration Define Knowledge Base Scope: Collaborate with domain experts to identify industry-specific requirements and tasks. Assess and select appropriate structured and unstructured data sources. Data Curation and Organization: Collect and preprocess data from authoritative sources (e.g., research papers, databases, manuals). Structure unstructured data using techniques like knowledge graphs. Implement data cleaning workflows to ensure high-quality input. Knowledge Integration: Embed knowledge bases into LLM workflows using tools like Pinecone, Weaviate, or Milvus. 2. LLM Fine-Tuning Fine-tune LLMs using frameworks such as Hugging Face Transformers or OpenAI APIs. Use domain-specific datasets to adapt base models to specialized industries. Apply transfer learning techniques to enhance model performance for niche applications. Monitor and improve fine-tuned models using validation metrics and feedback loops. 3. Prompt Engineering Design, test, and optimize prompts for industry-specific tasks. Implement contextual prompting strategies to enhance accuracy and relevance. Iterate on prompt designs based on system evaluations and user feedback. 4. Retrieval-Augmented Generation (RAG) Implement RAG workflows to integrate external knowledge bases with LLMs. Develop and optimize embedding-based retrieval systems using vector databases. Combine retrieved knowledge with user queries to generate accurate and context-aware responses. 5. System Integration Build APIs and middleware to interface between the LLM, knowledge base, and user-facing applications. Develop scalable and efficient query-routing mechanisms for hybrid retrieval and generation tasks. Ensure seamless deployment of LLM-powered applications. 6. Validation and Testing Evaluate model responses against domain-specific benchmarks and ground truths. Collaborate with domain experts to refine system outputs. Conduct user testing and gather feedback to improve system performance iteratively. 7. Maintenance and Updates Implement strategies to keep the knowledge base current with periodic updates. Develop monitoring tools to track system performance and identify areas for improvement. Address ethical, regulatory, and privacy considerations (e.g., GDPR, HIPAA compliance). Qualifications: Technical Skills Programming: Strong knowledge of Python and frameworks like Flask, FastAPI, or LangChain for API development. Data Preprocessing: Familiarity with preprocessing pipelines for structured and unstructured data. LLM Proficiency: Experience with LLM platforms such as OpenAI GPT, Hugging Face Transformers, or similar. Knowledge Base Management: Hands-on experience with vector databases (e.g., Pinecone, Milvus, Weaviate) and relational databases (e.g., PostgreSQL, MySQL). Fine-Tuning Expertise: Proficiency in adapting LLMs for specialized domains using domain-specific datasets. RAG Implementation: Practical experience with retrieval-augmented generation workflows. Prompt Engineering: Ability to craft and optimize prompts for complex, context-driven tasks. Soft Skills Strong problem-solving skills and attention to detail. Ability to collaborate effectively with cross-functional teams, including domain experts. Excellent communication and documentation skills. Experience 6+ years of experience in AI/ML roles, focusing on LLM agent development and deployment in recent years. 2 + years would creating AI solution with langChain, experience. Demonstrated experience in designing domain-specific AI systems. Hands-on experience in integrating structured/unstructured data into AI models.