Hiring for Returnship Only for Female Candidates. JD.Experience - 5-10yearsLevel - TM/SDMLocation - Pan IndiaNP - ImmediateGenAI or GenAI ArchitectBuild GenAI applications using Python for tasks like chatbots, summarization and intelligent automation.• Develop and fine tune LLMs and ML models for classification, prediction, and decision support.• Design solutions using embeddings, vector search, and retrieval augmented generation (RAG).• Deploy models using Azure Machine Learning and Azure OpenAI scale with Azure Functions and Cognitive Services.• Integrate models with AWS services like SageMaker, Lambda, Bedrock and data platforms like Snowflake.• Integrate AI systems with APIs, enterprise data platforms and business workflows.Strong Python development with experience in GenAI frameworks like LangChain, Hugging Face, OpenAI. • A. LLMs and hyperparameters (Azure / AWS / GCP / Open Source) • B. Embedding models and vector database knowledge • C. Prompting Techniques (Zero shot, few shot, chain of thought) • D. Frameworks: Langchain, Pydantic • E. RAG, Problem solving skills on where to apply RAG / Other Gen AI techniques. • B. Frameworks like Pandas, Fast API, NumpyPreferred Skills• Solid foundation in ML algorithms, training pipelines and evaluation techniques.• Familiarity with prompt engineering, tokenization and model optimization.• Hands-on with Azure cloud tools for model lifecycle, deployment and serverless execution.• Ability to connect models to data sources, automation tools and orchestration platforms.System Design: Develop and design the architecture for AI systems, ensuring they integrate seamlessly with business operations.2. Technology Selection: Choose appropriate technologies and tools for building and deploying generative AI solutions.3. Scalability: Ensure the AI systems are scalable and can handle increasing workloads efficiently.4. Model Management: Oversee the lifecycle of generative AI models, including development, deployment, and maintenance.5. Prompt Engineering: Design and refine prompts used in natural language processing models to optimize performance.6. Data Integration: Integrate data from various sources to support AI model training and inference.7. Performance Optimization: Continuously monitor and optimize the performance of AI models and systems.8. Security and Compliance: Ensure AI systems adhere to security protocols and compliance standards.9. Collaboration: Work closely with data scientists, ML engineers, and other stakeholders to align AI solutions with business goals.10. Innovation: Stay updated with the latest advancements in AI and incorporate innovative solutions into the architecture. About Cognizant: Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant. Additional employment information Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law. Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview. Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws.