Description

Key Skills: Amazon Bedrock, AWS, Machine Learning, Deep Learning, Python, AI Architecture, MLOps, Generative AI, Cloud Architecture, Data Engineering

Good to Have Skills: Experience with cloud-native AI services such as model hosting, autoML, vector search, and GPU workloads. Familiarity with MLOps tools including MLflow, Kubeflow, SageMaker Pipelines, and Azure ML Pipelines. Experience with LLM architectures, RAG pipelines, and production-grade GenAI implementations. Certifications in AI, cloud architecture, or data engineering are preferred.

Roles & Responsibilities:

• Design scalable, secure, and high-performance AI/ML architectures aligned with organizational goals and business requirements.
• Build reference architectures, solution blueprints, and reusable frameworks for AI workloads across the organization.
• Partner with data scientists and engineers to operationalize machine learning models at scale for production deployment.
• Design model training, validation, testing, deployment, and monitoring workflows to ensure optimal performance and reliability.
• Define and implement MLOps best practices, including CI/CD automation for AI systems and continuous integration processes.
• Architect data pipelines and feature stores that support model training and real-time inference capabilities.
• Ensure high-quality data ingestion, transformation, governance, and lineage tracking across all AI systems.
• Collaborate with data engineering teams to optimize data accessibility and performance for AI workloads.
• Establish AI governance principles, including responsible AI, model explainability, and auditability frameworks.
• Implement secure designs including identity, access control, encryption, and threat monitoring for AI systems.
• Advise executives and business leaders on AI strategy, opportunities, and risks in the current market landscape.
• Work with product and engineering teams to embed AI capabilities into applications and business processes.
• Mentor technical teams and provide thought leadership on emerging AI technologies and industry best practices.

Experience Required: 10+ years of total experience with at least 2-3 years in AI/ML/GenAI. 7+ years of experience in architecture, software engineering, or AI/ML solution delivery. Strong knowledge of machine learning principles, deep learning techniques, and generative AI. Hands-on experience designing and deploying AI systems in cloud or hybrid environments.

Education: Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field

Key Skills

Education

Any Graduate