Principal AI Architect

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