Hiring for IOA Returnship Program

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.