Description

Design end-to-end AI/ML architectures including data ingestion, feature engineering, model development, deployment, monitoring, and retraining. Define enterprise AI reference architectures for GenAI, ML, NLP, computer vision, and predictive analytics use cases. Select and standardize AI platforms, frameworks, and tools such as cloud AI services, MLOps platforms, and LLM frameworks. Partner with business, data, and engineering leaders to translate use cases into technical AI solutions. Guide AI adoption across ETL, data platforms, and analytics ecosystems, supporting proof of concepts and pilots.

Responsibilities

• Design and implement scalable AI/ML architectures for production environments.
• Define enterprise reference architectures for Generative AI, NLP, and predictive analytics.
• Select and standardize AI platforms, MLOps tools, and LLM frameworks.
• Partner with engineering leaders to translate business use cases into technical solutions.
• Guide AI adoption across ETL and analytics ecosystems through pilots and POCs.

Required Skills

• 8+ years of experience in data, analytics, or platform architecture roles.
• 4+ years designing and implementing AI/ML solutions at scale with proven production deployment.
• Strong proficiency in Python and SQL.
• Deep knowledge of Machine Learning, Deep Learning, and Generative AI concepts.
• Experience with cloud AI platforms (Azure, AWS, GCP).
• Hands-on experience with MLOps tools and data engineering pipelines.
• Understanding of APIs, microservices, containers, and orchestration.

Preferred Skills

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.

Key Skills

Education

Any Graduate