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.
コグニザントについて
コグニザント（NASDAQ: CTSH）は、AI Builderおよびテクノロジーサービスプロバイダーとして、お客様にフルスタックのAIソリューションを構築することで、AI投資と企業価値を結ぶ架け橋となっています。業界、ビジネスプロセス、エンジニアリングに関する当社の深い専門知識を活かし、組織固有のビジネス環境をテクノロジー・システムに組み込みます。これにより、人間の可能性を最大限に引き出し、確かな成果を実現するとともに、急速に変化する世界においてグローバル企業が常に一歩先を行くための支援を行っています。 詳細については、cognizant.ai をご覧ください。
雇用に関する追加情報
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