Gen AI Architect (ATC)
Experience Level 10+ years (with at least 2–3 years in AI/ML/GenAI)
Primary Skill Amazon Bedrock, AWS
Responsibilities
- AI & Solution Architecture
* Design scalable, secure, and high‑performance AI/ML architectures aligned with organizational goals.
* Build reference architectures, solution blueprints, and reusable frameworks for AI workloads.
- Model Development & Deployment
Partner with data scientists and engineers to operationalize machine learning models at scale.
Design model training, validation, testing, deployment, and monitoring workflows.
Define and implement MLOps best practices, including CI/CD automation for AI systems.
Data Architecture & Integration
Architect data pipelines and feature stores that support model training and real-time inference.
Ensure high-quality data ingestion, transformation, governance, and lineage tracking.
Collaborate with data engineering teams to optimize data accessibility and performance.
Governance, Security & Compliance
Establish AI governance principles, including responsible AI, model explainability, and auditability.
Implement secure designs including identity, access control, encryption, and threat monitoring.
Stakeholder Collaboration & Leadership
Advise executives and business leaders on AI strategy, opportunities, and risks.
Work with product and engineering teams to embed AI capabilities into applications.
Mentor technical teams and provide thought leadership on emerging AI technologies.
Required Qualifications
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
Preferred Qualifications
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.
Certifications in AI, cloud architecture, or data engineering.