Principal AI Architect

Job description

Key Responsibilities 1. AI, ML & Generative AI Architecture • Define end-to-end architecture for AI/ML and Generative AI systems including data ingestion, feature engineering, model training, deployment, monitoring, and governance • Design and implement scalable Lakehouse-based AI platforms using Databricks and Snowflake • Architect solutions supporting both batch and real-time inference workloads • Lead the design of enterprise-grade GenAI applications using LLMs, RAG pipelines, and Agentic AI frameworks • Establish architectural standards, best practices, and reusable AI frameworks 2. RAG, LLM & Agentic AI Solutions • Design and implement Retrieval-Augmented Generation (RAG) architectures using vector databases and knowledge pipelines • Architect intelligent AI agents for automation, orchestration, and decision-making workflows • Evaluate and integrate LLMs (OpenAI, LLaMA, etc.) for enterprise use cases • Optimize prompt engineering, embeddings, and context management strategies • Ensure scalability, accuracy, and cost optimization in GenAI deployments 3. Data & 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 quality frameworks 4. Cloud & Platform Architecture • Architect AI solutions on cloud platforms such as AWS, Azure, or GCP • Design cloud-native, microservices-based AI systems • Leverage containerization and orchestration tools (Docker, Kubernetes) for scalable deployments • Implement MLOps and LLMOps best practices for CI/CD, monitoring, and lifecycle management 5. POCs, Innovation & Technical Leadership • Conduct Proof of Concepts (POCs) to validate architectural approaches and design considerations • Analyze current product architecture and recommend AI-driven enhancements • Provide technical leadership and mentorship to AI, Data Science, and Engineering teams • Drive innovation by identifying emerging AI/GenAI trends and enterprise adoption opportunities • Collaborate with stakeholders, product managers, and business leaders to translate business needs into AI solutions 6. Governance, Security & Compliance • Define AI governance frameworks including model monitoring, explainability, and ethical AI practices • Ensure compliance with data privacy and enterprise security standards • Implement observability, model performance tracking, and risk mitigation strategies Required Skills & Qualifications • 12+ years of experience in AI/ML architecture, Data Engineering, or Advanced Analytics • Strong expertise in Generative AI, LLMs, RAG, and Agentic AI architectures • Hands-on experience with Databricks, Snowflake, and Lakehouse architecture • Proficiency in Python, PySpark, and AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn) • Experience with Vector Databases (FAISS, Pinecone, Weaviate, etc.) • Strong knowledge of MLOps/LLMOps tools such as MLflow, Kubeflow, or Azure ML • Experience designing real-time and batch AI pipelines • Deep understanding of Feature Engineering and model lifecycle management • Strong experience with REST APIs, microservices, and scalable system design