Role: Generative AI Architect
Location: Bangalore/Gr. Noida
JD:
The Generative AI Architect will be responsible for designing, developing, and implementing generative AI solutions that align with the company's strategic objectives. This role involves leading the architecture and deployment of advanced AI models, including LLM-based systems and agentic workflows, ensuring scalability, security, and ethical considerations are integrated into all AI initiatives. The ideal candidate will possess deep expertise in generative AI technologies, LLM orchestration frameworks (e.g., LangChain, LangGraph), a strong understanding of AI ethics, and the ability to collaborate effectively with cross-functional teams
Key Responsibilities
Architecture Design
- Develop and maintain the architectural framework for generative AI and LLM-powered solutions (including RAG, agents, and multi-step workflows), ensuring alignment with business goals and technical standard
Model Development
- Lead the design, fine-tuning, and deployment of generative AI models (e.g., GPT, DALL-E, Stable Diffusion) and LLM-based applications for use cases such as content generation, automation, copilots, and decision support systems
LLM Orchestration & Agent Design
- Design and implement LLM orchestration pipelines using frameworks such as LangChain, LangGraph, Semantic Kernel, or similar tools. Build agentic workflows, tool-augmented agents, and multi-step reasoning pipeline
RAG & Knowledge System
- Architect and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases (e.g., FAISS, Pinecone, Azure AI Search) and embedding models to enable enterprise knowledge integration
Integration
- Collaborate with software engineering teams to integrate generative AI capabilities, LLM services, and autonomous agents into existing applications, APIs, and enterprise workflow
Scalability & Performance
- Ensure AI/LLM solutions are scalable, optimized for latency and cost, and production-ready across cloud and hybrid environment
Ethical AI Practices
- Implement and enforce ethical guidelines for AI development, including bias mitigation, explainability, safety guardrails, and responsible AI practices in LLM deployment
Research & Innovation
- Stay abreast of advancements in LLMs, prompt engineering, agent frameworks, multimodal AI, and GenAI tooling, incorporating emerging techniques into the AI strategy
Collaboration
- Work closely with data scientists, ML engineers, product teams, and stakeholders to identify AI-driven opportunities and ensure successful delivery
Documentation & Standard
- Create comprehensive documentation for AI architectures, LLM workflows, prompt templates, and best practices. Establish coding, evaluation, and governance standards
Experience
- Minimum of 7+ years of experience in AI/ML architecture or a related role
- Proven experience in designing and deploying Generative AI and LLM-based solutions in production
- Hands-on experience with LangChain, LangGraph, or similar orchestration frameworks for building LLM pipelines and agent systems
- Experience in building RAG pipelines, vector database integration, and prompt engineering techniques
- Strong experience with AI frameworks/tools such as TensorFlow, PyTorch, Hugging Face
- Experience with cloud platforms (AWS, Azure, GCP) and deploying scalable AI/LLM solutions
Technical Skills
- Proficiency in programming languages such as Python (preferred), Java, or C++
- Strong expertise in LLMs, prompt engineering, embeddings, and fine-tuning techniques
Hands-on experience with:
- LangChain, LangGraph / Agent frameworks
- Vector databases (Pinecone, FAISS, Weaviate, Azure AI Search)
- API integration & microservices architecture
- Strong understanding of machine learning and deep learning concepts
