Description

You will design and implement scalable, secure AI/ML architectures and reference blueprints for production-grade generative AI systems.

This role is on-site.

Responsibilities

• Architect data pipelines, feature stores, and RAG pipelines to support model training and real-time inference.
• Define and implement MLOps best practices, including CI/CD automation, model monitoring, and governance frameworks.
• Partner with data scientists and engineers to operationalize machine learning models and embed AI capabilities into applications.
• Establish AI governance principles, ensuring responsible AI, model explainability, and secure design patterns.

Required Skills

• 10+ years of total IT experience, with 7+ years in architecture or software engineering.
• 2-3 years of hands-on experience in AI/ML/GenAI, including LLM architectures and deep learning.
• Strong proficiency in Python and cloud architecture (AWS).
• Experience with Amazon Bedrock, Generative AI, and Machine Learning principles.
• Knowledge of MLOps tools such as MLflow, Kubeflow, or SageMaker Pipelines.
• Understanding of data engineering, vector search, and GPU workloads.

Preferred Skills

• Certifications in AI, cloud architecture, or data engineering.
• Familiarity with Azure ML Pipelines and autoML services.

Key Skills

Education

Any Graduate