- Architect and develop scalable, high-performance Generative AI solutions on Azure/AWS/ GCP platforms
- Design, build, and manage containerized AI applications using Docker and orchestrate deployments via Kubernetes
- Establish and maintain observability frameworks for LLMs, including performance monitoring, system health, and usage analytics
- Develop and implement machine learning models using Python or R, leveraging frameworks such as TensorFlow, PyTorch, or scikit-learn
- Build and deploy AI/ML solutions using cloud-native services such as AWS SageMaker, Azure AI, or Google Vertex AI
- Design and implement multi-agent systems and Retrieval-Augmented Generation (RAG) architectures
- Define and enforce standards for prompt engineering, vector search, and model orchestration
- Build and manage enterprise-grade MLOps pipelines covering model training, fine-tuning, evaluation, deployment, and monitoring
- Collaborate with cross-functional teams to integrate AI capabilities into enterprise products and platforms
- Lead system optimization, performance tuning, and troubleshooting of AI-driven applications
- Establish best practices for secure, scalable, and maintainable AI architectures
- Mentor and guide AI engineering teams on emerging technologies and implementation best practices
- Stay current with advancements in Generative AI, Agentic AI, and cloud technologies
📌 Gen AI Architect (Gurugram)
🏢 Cognizant
📍 Gurugram
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