Job Title : AI Architect – Generative AI
Experience : 8–10 Years
Location : Remote (Contractual Role)
Duration : 12 months
Type : Contract (Full-time / Part-time – depending on your need)
Job Summary :
We are seeking a highly experienced AI Architect with a strong background in AI / ML systems architecture and Generative AI technologies to lead the design and implementation of innovative, AI-driven solutions. The ideal candidate will be responsible for driving end-to-end AI architecture, with a strong focus on LLMs, transformer models, embeddings, RAG pipelines , and deploying enterprise-ready AI applications at scale.
Key Responsibilities :
- Design, develop, and oversee scalable AI / ML architectures across cloud and hybrid environments.
- Lead the implementation of Generative AI solutions , including LLM-based applications (e.g., GPT, LLaMA, Claude).
- Architect and optimize Retrieval-Augmented Generation (RAG) pipelines and vector database integration.
- Collaborate with product, data science, and engineering teams to translate business requirements into robust AI systems.
- Evaluate and integrate open-source and commercial GenAI models (e.g., Hugging Face, OpenAI, Anthropic).
- Establish best practices for model fine-tuning , prompt engineering , and AI safety / compliance .
- Drive PoC to production lifecycle for GenAI tools like chatbots, document summarizers, copilots, etc.
- Maintain technical documentation, governance frameworks, and model performance monitoring.
Must-Have Skills :
8–10 years of experience in AI / ML development and architecture .Hands-on experience with Generative AI models like GPT, BERT, LLaMA, Claude, etc.Deep knowledge of LLMs , transformer architectures , embedding models , and fine-tuning techniques .Expertise in Python , TensorFlow / PyTorch , and Hugging Face Transformers .Experience with LangChain , LLM orchestration , and vector DBs like Pinecone , FAISS , or Weaviate .Solid understanding of cloud services (AWS / GCP / Azure), Kubernetes, and scalable model deployment strategies.Familiarity with MLOps practices , including CI / CD for ML, experiment tracking, and monitoring.Strong knowledge of data privacy , AI ethics , and responsible AI frameworks .Preferred Skills :
Exposure to multi-modal models (vision + language).Experience deploying enterprise GenAI use cases (e.g., document processing, customer support bots, knowledge assistants).Contributions to open-source GenAI frameworks or communities.Strong communication and stakeholder management skills.