Principal AI Architect

Role : Principal AI Architect

We are seeking a highly experienced and visionary Principal AI Architect to design, lead, and implement enterprise-scale AI, Machine Learning, and Generative AI solutions. The ideal candidate will have strong expertise in building scalable AI platforms using Databricks, Snowflake, and cloud-native ecosystems, along with hands-on experience in Feature Engineering, RAG, LLMs, and Agentic AI architectures.

Key Responsibilities : AI/ML and GenAI Architecture :

• Define end-to-end architecture for AI/ML and Generative AI systems.

• Design scalable Lakehouse-based AI platforms using Databricks and Snowflake.

• Architect solutions supporting batch and real-time inference workloads.

• Design enterprise-grade LLM, RAG, and Agentic AI applications.

• Establish architectural standards, best practices, and reusable AI frameworks.

RAG, LLM and Agentic AI :

• Design and implement Retrieval-Augmented Generation architectures using vector databases and knowledge pipelines.

• Architect intelligent AI agents for automation, orchestration, and decision-making workflows.

• Evaluate and integrate LLMs such as OpenAI and LLaMA.

• Optimize prompt engineering, embeddings, context management, scalability, accuracy, and cost.

• Develop enterprise-ready GenAI solutions.

Data and Feature Engineering :

• Design robust data pipelines for structured and unstructured data.

• Lead feature engineering strategies for ML and AI models.

• Collaborate with Data Engineering teams to build high-performance data ingestion and transformation pipelines.

• Implement data governance, lineage, and data quality frameworks.

Cloud and AI Platform Architecture :

• Architect AI solutions on AWS, Azure, or GCP.

• Design cloud-native and microservices-based AI systems.

• Leverage Docker and Kubernetes for scalable AI deployments.

• Implement MLOps and LLMOps best practices for CI/CD, monitoring, and lifecycle management.

Technical Leadership and Innovation :

• Conduct Proof of Concepts to validate architectural approaches and design decisions.

• Analyze existing product architecture and recommend AI-driven enhancements.

• Provide technical leadership and mentorship to AI, Data Science, and Engineering teams.

• Identify emerging AI and GenAI trends and enterprise adoption opportunities.