Hiring for IOA Returnship Program

Cognizant

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.