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 standards.

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 pipelines.

RAG &

- Knowledge Systems:

- 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, nd autonomous agents into existing applications, APIs, and enterprise workflows.

Scalability &

- Performance:

- Ensure AI/LLM solutions are scalable, optimized for latency and cost, and production-ready across cloud and hybrid environments.

Ethical AI Practices:

- Implement and enforce ethical guidelines for AI development, including bias mitigation, explainability, safety guardrails, and responsible AI practices in LLM deployments.

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 &

- Standards:

- 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 RAG pipelines 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

- Solid understanding of machine learning and deep learning concepts

📌 Senior Generative Architect (Hyderabad)
🏢 Coforge
📍 Hyderabad