Hiring for IOA Returnship Program

Job description

Hiring for Returnship Only for Female Candidates. JD. Experience - 5-10years Level - TM/SDM Location - Pan India NP - Immediate GenAI or GenAI Architect Build 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, Numpy Preferred 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. • Technology Selection: Choose appropriate technologies and tools for building and deploying generative AI solutions. • Scalability: Ensure the AI systems are scalable and can handle increasing workloads efficiently. • Model Management: Oversee the lifecycle of generative AI models, including development, deployment, and maintenance. • Prompt Engineering: Design and refine prompts used in natural language processing models to optimize performance. • Data Integration: Integrate data from various sources to support AI model training and inference. • Performance Optimization: Continuously monitor and optimize the performance of AI models and systems. • Security and Compliance: Ensure AI systems adhere to security protocols and compliance standards. • Collaboration: Work closely with data scientists, ML engineers, and other stakeholders to align AI solutions with business goals. • Innovation: Stay updated with the latest advancements in AI and incorporate innovative solutions into the architecture.