Data & AI Architect

Key Responsibilities

• Design end-to-end data architecture, data platforms, and AI solutions for enterprise applications.
• Architect modern data warehouses, data lakes, and lakehouse platforms.
• Design scalable data engineering and ETL/ELT pipelines for structured and unstructured data.
• Define data models, data integration patterns, data governance, metadata, and data quality frameworks.
• Develop solutions for data ingestion, transformation, processing, and analytics.
• Architect and implement AI/ML and Generative AI solutions using enterprise data.
• Design RAG (Retrieval-Augmented Generation) architectures using enterprise documents and structured data.
• Work with LLMs, vector databases, embeddings, prompt engineering, and AI orchestration frameworks.
• Evaluate and select appropriate cloud data and AI services based on business requirements.
• Ensure solutions meet requirements for security, scalability, performance, reliability, and cost optimization.
• Establish best practices for data governance, privacy, lineage, security, and lifecycle management.
• Collaborate with data engineers, data scientists, ML engineers, software engineers, and business stakeholders.
• Provide technical leadership and mentor engineering teams on data and AI architecture.

Required SkillsData Architecture & Warehousing

• Strong experience in Data Warehousing and Data Architecture.
• Expertise in dimensional modeling, data modeling, data marts, and enterprise data platforms.
• Strong understanding of Data Lakes, Lakehouse architecture, and modern data platforms.
• Experience with technologies such as Snowflake, Databricks, BigQuery, Redshift, Synapse, or similar platforms. Data Engineering
• Strong experience with ETL/ELT pipelines and data integration.
• Hands-on experience with SQL and Python.
• Experience with Apache Spark, Kafka, Airflow, dbt, or similar technologies.
• Understanding of batch and real-time/streaming data processing.
• Experience integrating APIs, databases, files, and cloud data sources. Data Management
• Strong understanding of data governance, data quality, metadata management, data lineage, and master/reference data management.
• Experience defining data standards, security controls, and data lifecycle policies.
• Knowledge of enterprise data management frameworks and best practices. AI & Generative AI
• Strong understanding of AI/ML concepts and Generative AI architectures.
• Experience working with LLMs, RAG, embeddings, vector databases, and prompt engineering.
• Experience with frameworks/tools such as LangChain, LangGraph, LlamaIndex, or equivalent.
• Experience integrating OpenAI, Azure OpenAI, Google Vertex AI, AWS Bedrock, or equivalent GenAI platforms.
• Understanding of AI agents/Agentic AI, model evaluation, guardrails, and responsible AI is preferred. Cloud & Architecture
• Experience with at least one major cloud platform: AWS, Azure, or GCP.
• Strong understanding of cloud-native architecture, APIs, microservices, security, and CI/CD.
• Ability to design highly scalable, secure, and cost-effective enterprise solutions.

Qualifications

• 8+ years of overall experience in data engineering, data architecture, AI/ML, or related technology domains.
• 3+ years of experience in data architecture/data engineering.
• Hands-on experience designing and implementing enterprise data platforms.
• Experience delivering AI/GenAI solutions in production environments.
• Strong architecture, problem-solving, communication, and stakeholder-management skills. Skills: data architecture,data,design,enterprise data,architecture,enterprise,data engineering,ai