AI/ML Architect

Job Description:

We are seeking an experienced AI/ML Architect with a strong background in Data Engineering to design and implement scalable, cloud-native data and AI solutions. The ideal candidate will have hands-on experience building modern data platforms, developing enterprise-grade data pipelines, and integrating AI/ML capabilities into business applications.

The candidate should have strong expertise in data architecture, cloud technologies, Generative AI, and enterprise AI solutions, with the ability to lead architecture discussions and collaborate with data engineering, AI/ML, and business teams.

Key Responsibilities

• Design and implement end-to-end data and AI/ML architectures, ensuring scalability, reliability, performance, and security.
• Architect modern data platforms, including data lakes, data warehouses, and lakehouse solutions.
• Design and implement AI/ML and Generative AI solutions, including LLM-based applications, RAG architectures, and AI agents.
• Integrate enterprise data platforms with AI/ML models to enable intelligent applications and data-driven decision-making.
• Define data governance, data quality, metadata management, security, and access control standards.
• Evaluate and recommend appropriate cloud services, AI/ML frameworks, and data engineering technologies.
• Provide technical leadership and architectural guidance to data engineers, AI/ML engineers, and development teams.
• Collaborate with business stakeholders to translate business requirements into scalable technical solutions.

Required Skills and Qualifications

• Strong experience in Data Engineering, Data Architecture, and AI/ML solution design.
• Hands-on expertise in Python, SQL, and distributed data processing frameworks such as Apache Spark and PySpark.
• Experience designing and implementing data pipelines using technologies such as Apache Airflow, Kafka, or equivalent platforms.
• Strong understanding of data lakes, data warehouses, lakehouse architecture, and modern data engineering practices.
• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Strong knowledge of AI/ML concepts, model development, deployment, and MLOps practices.
• Experience with Generative AI, Large Language Models (LLMs), RAG, vector databases, and AI application frameworks.
• Understanding of data governance, data security, data quality, and enterprise architecture principles.
• Excellent problem-solving, communication, and technical leadership skills.