Role : GEN AI Technical Architect

Required : LLM, AI/ML Engineering, Prompt Engg, RAG, Python, CICD

Notice : Immediate to 30 days ideal

Experience : 10 years

Location : Multiple

Technical Expertise :

- Experience : Minimum of 10 years of experience in Technical Architecture, Data Architecture, or ML Engineering, with a minimum of 3 years dedicated to architecting production-grade Generative AI.

- Generative AI : Deep, hands-on expertise with LLMs, Transformer architectures, Fine-Tuning/Transfer Learning, and complex techniques like RAG and advanced Prompt Engineering.

- Cloud Platforms : Expert-level proficiency with a major cloud provider (AWS, Azure, or GCP) and their respective AI/ML service offerings (e.g., Amazon Bedrock, Azure OpenAI Service, Google Vertex AI).

- Programming : Mastery of Python, including relevant data science and ML libraries (PyTorch, TensorFlow).

- Data Systems : Proven experience designing data pipelines for GenAI, including vectorization, embedding models, and integration with modern data architectures (data lakes, data meshes).

- DevOps/MLOps : Strong understanding of containerization (Docker, Kubernetes) and MLOps tools for managing the lifecycle of production AI models.

Key Responsibilities :

- Architect and deploy end-to-end Generative AI solutions by integrating Large Language Models (LLMs) into existing enterprise ecosystems to solve high-impact business problems.

- Design and optimize Retrieval-Augmented Generation (RAG) pipelines to ensure high-accuracy, context-aware responses that meet stringent enterprise data standards.

- Lead the technical strategy for Vector Database selection and implementation to facilitate efficient information retrieval and long-term memory for AI agents.

- Mentor engineering teams on advanced Prompt Engineering techniques and model fine-tuning strategies to improve output quality and reduce latency.