4.0 - 8.0 years

10 - 15 Lacs

Bengaluru

Work from Office

Key Responsibilities: Design and Implement RAG-Based Solutions: Lead the development of a robust RAG-based system that seamlessly integrates generative AI models with retrieval mechanisms, ensuring optimal accuracy, performance, and scalability. Agent-Based Solution Development : Build AI agents that can autonomously perform tasks using information retrieval, language models, and multi-turn interactions using AutoGen, LangGraph frameworks. Generative AI Integration : Leveraging LLMs (e.g., GPT, Anthropic, Llama, Deepseek etc.) for generating high-quality, contextually accurate content in response to user queries. Prompt Engineering: Strong proficiency in prompt engineering to design, test, and optimize prompts for large language models, ensuring effective agent behavior and task completion Data & Knowledge Integration : Work on integrating structured and unstructured data sources into the RAG framework to improve model outputs. Collaborate with Teams : Work closely with product, engineering, and data science teams to ensure seamless integration of AI capabilities into broader products. Security & Compliance : Ensure following best practices in data security, privacy, and compliance, particularly in AI-driven applications. Model Evaluation : Implement strategies for continuous evaluation, monitoring, and tuning of models to ensure high-quality outputs. Data Pipeline Design : Build robust data pipelines for preprocessing and integrating structured and unstructured data sources into the platform. Collaborate with Teams : Work closely with data engineers, and software engineers to ensure seamless integration of machine learning models into production environments. Performance Optimization : Implement and experiment with various techniques such as vector search , semantic search , and knowledge distillation to enhance the platforms efficiency and accuracy. Data Quality : Ensure data quality and consistency throughout the data pipeline, implementing necessary quality checks and validation processes. Requirements: 4+ years of experience with building GenAI solutions using Python and Langchain and AutoGen technologies. Agentic AI experienced developing agents using AutoGen and LangGraph frameworks. Prior experience in building GenAI solutions using effective text representation techniques and classification algorithms. Experience with ML – Model Building, Semantic extraction techniques, data structures and modelling Experience with Supervised/Unsupervised Learning, Sentiment Analysis, Statistical Analysis using NLP and Python libraries (NLTK, Spacy, Numpy, Pandas) Experienced in building Rest API’s Experience with building applications with micro services & serverless architecture Excellent communication skills Good to have: Experience with Azure cloud services Experience with Azure Foundry & Prompt flow

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