Generative AI Architect (10-17 yrs)

Experience Required : 10 - 17 Years.

Overview :

This is an opportunity to be part of a dynamic team focused on building cutting-edge AI-driven solutions, including AI agents, autonomous workflows, and generative AI applications.

As a Generative AI Architect, you will play a crucial role in defining, designing, and implementing AI-powered architectures that drive business transformation.

You will work with cross-functional teams, including data scientists, ML engineers, software developers, and business stakeholders, to create scalable AI solutions that integrate seamlessly with existing enterprise applications.

This role requires a strategic thinker with deep hands-on experience in AI technologies, cloud platforms, and modern software architectures.

Key Responsibilities :

- Define & Design AI Architectures : Establish scalable, modular, and efficient architectures for AI agents, LLM-based applications, and generative AI models.

- Hands-on AI Development : Implement and optimize generative AI models, leveraging frameworks such as OpenAI, LangChain, LlamaIndex, AutoGen or AG2, RAG, and multi-agent systems.

- AI Integration & Deployment : Work with cloud AI platforms ( Azure, GCP) and implement best practices for MLOps, model serving, and continuous improvement.

- Enterprise AI Strategy : Align AI-driven solutions with business objectives, ensuring scalability, security, and cost-effectiveness.

- Cross-functional Collaboration : Engage with product teams, data scientists, and engineering teams to ensure seamless integration of AI-powered solutions into business workflows.

- AI Governance & Ethics : Establish responsible AI practices, ensuring compliance with data privacy, model bias mitigation, and security best practices.

- Evaluation & Optimization : Continuously assess AI agent performance and optimize architectures for speed, efficiency, and cost-effectiveness.

Must-Have :

- Hands-on experience in designing and deploying AI-powered agents, multi-agent architectures, or autonomous systems.

- Expertise in Large Language Models (LLMs) such as GPT, BERT, Llama, Gemini, and their deployment at scale.

- Proficiency in AI frameworks like AutoGen/AG2, TensorFlow, PyTorch, Hugging Face, LangChain, and RAG-based architectures.

- Strong knowledge of retrieval-augmented generation (RAG), vector databases (Azure AI Search, Pinecone, FAISS, Chroma), and prompt engineering techniques.

- Experience with cloud AI services (Azure OpenAI, Google Vertex AI) and containerized deployment using Docker, Kubernetes.

- Strong background in API-driven development, microservices, and event-driven architectures.

- Experience with MLOps, CI/CD pipelines, monitoring AI models in production, and fine-tuning custom AI models.

- Ability to work with unstructured data, embeddings, and knowledge graphs for AI-driven insights.

- Excellent problem-solving, analytical, and communication skills to work effectively with technical and non-technical teams.