Data Architect - AI/ML & Data Platforms

Job Summary:

We are seeking an experienced AI Data Architect to lead the design, development, and implementation of enterprise-scale AI and Data platforms. The ideal candidate will have strong expertise in Data Architecture, Cloud Technologies, Machine Learning, Generative AI, Data Engineering, and MLOps. This role requires collaboration with business stakeholders, data scientists, engineers, and technology leaders to build scalable AI-driven solutions that drive business transformation.

Key Responsibilities:

- Design and implement enterprise AI and Data Architecture strategies aligned with business objectives.

- Define scalable data models, data governance frameworks, metadata management, and data quality standards.

- Architect and oversee modern data platforms using cloud technologies (AWS, Azure, or GCP).

- Lead the development of AI/ML and Generative AI solutions for business use cases.

- Design and implement data lakes, lakehouses, data warehouses, and real-time data pipelines.

- Establish MLOps and LLMOps frameworks for deployment, monitoring, and governance of AI models.

- Define architecture for RAG (Retrieval-Augmented Generation), Vector Databases, AI Agents, and LLM-powered applications.

- Collaborate with Data Scientists and Engineering teams to operationalize AI solutions.

- Drive data security, compliance, privacy, and governance initiatives.

- Evaluate emerging AI technologies, tools, and frameworks and recommend best-fit solutions.

- Provide technical leadership, architecture reviews, and mentorship to development teams.

- Engage with stakeholders to translate business requirements into scalable AI and data solutions.

Required Skills:

1. Data Architecture:

- Enterprise Data Architecture

- Data Modeling (Conceptual, Logical, Physical)

- Data Warehousing

- Data Lakes / Lakehouse Architecture

- Master Data Management (MDM)

- Data Governance & Data Quality

2. AI & Machine Learning:

- Machine Learning Lifecycle

- Deep Learning Concepts

- NLP (Natural Language Processing)

- Generative AI

- Large Language Models (LLMs)

- Prompt Engineering

- RAG Architecture

- AI Agents & Agentic AI

3. Data Engineering:

- ETL/ELT Frameworks

- Apache Spark

- Databricks

- Kafka

- Real-Time Data Processing

- Python

- SQL

4. Cloud Platforms:

- AWS / Azure / GCP

- Cloud-Native Data Services

- Serverless Architecture