Gen AI Architect

6w1 month ago

Virtusa

Hyderābād, IN · Full-time · INR 3,000,000 – INR 4,500,000

About this role

As a Gen AI Architect, you will design scalable, secure, and high-performance AI/ML architectures aligned with organizational goals. This role requires deep expertise in cloud environments, particularly Amazon Bedrock and AWS.

You will partner with data scientists and engineers to operationalize machine learning models at scale, defining and implementing MLOps best practices including CI/CD automation. Architecting data pipelines and feature stores for real-time inference is a core daily responsibility.

Collaboration with data engineering teams ensures optimized data accessibility and performance. You will also establish AI governance principles covering responsible AI, model explainability, and auditability.

Advising executives on AI strategy and mentoring technical teams provides opportunities for thought leadership on emerging generative AI technologies. This role offers the chance to shape enterprise AI adoption.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
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.
Proficiency in Python or similar languages used in AI/ML development.
Experience with cloud-native AI services (e.g., model hosting, autoML, vector search, GPU workloads).
Familiarity with MLOps tools (MLflow, Kubeflow, SageMaker Pipelines, Azure ML Pipelines, etc.).
Experience with LLM architectures, RAG pipelines, and production-grade GenAI implementations.

Responsibilities

Design scalable, secure, and high-performance AI/ML architectures aligned with organizational goals.
Build reference architectures, solution blueprints, and reusable frameworks for AI workloads.
Partner with data scientists and engineers to operationalize machine learning models at scale.
Define and implement MLOps best practices, including CI/CD automation for AI systems.
Architect data pipelines and feature stores that support model training and real-time inference.
Establish AI governance principles, including responsible AI, model explainability, and auditability.
Advise executives and business leaders on AI strategy, opportunities, and risks.
Mentor technical teams and provide thought leadership on emerging AI technologies.