Cognizant

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Hiring for IOA Returnship Program

Open 22d


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.

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.


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