Senior AI Architect

Job Title: Senior AI Architect

Location: Washington, DC
 

Experience Required

• 8–10+ years of experience in Software Engineering or Data Architecture.

• Minimum 4+ years of hands-on experience designing, developing, and deploying AI/ML systems in production environments.

Required Skills & Qualifications

Enterprise AI Strategy

• Lead the transition of AI initiatives from localized Proof of Concepts (PoCs) to scalable, enterprise-wide production systems.

AI Technology Selection

• Evaluate and recommend the most appropriate AI technologies, including Deep Learning, Natural Language Processing (NLP), Computer Vision, and Generative AI, based on business requirements, cost, latency, scalability, and data privacy considerations.

AI Solution Architecture

• Design end-to-end AI architectures, including data ingestion, model training and fine-tuning, deployment, monitoring, and lifecycle management.

MLOps & Infrastructure

• Design and implement robust MLOps practices, including Continuous Integration (CI), Continuous Deployment (CD), and Continuous Training (CT), to ensure reliable model performance and prevent model drift.

Data & Integration

• Collaborate closely with Data Engineering teams to design scalable AI data architectures, including Vector Databases, Retrieval-Augmented Generation (RAG) workflows, and enterprise data pipelines.

AI Governance & Ethics

• Establish AI governance frameworks and guardrails to ensure models are fair, transparent, secure against adversarial attacks, and compliant with data privacy regulations such as GDPR and CCPA.

Cost Management

• Demonstrated experience optimizing AI infrastructure costs, including GPU resource allocation, token optimization, and efficient management of large-scale AI workloads.

Database & Data Warehousing

• Strong understanding of SQL and modern data warehouse platforms such as Snowflake and BigQuery for AI data pipeline orchestration.

Communication & Leadership

• Exceptional communication skills with the ability to explain complex AI concepts, capabilities, and limitations to executive leadership and non-technical stakeholders.

• Proven leadership experience driving enterprise AI initiatives across cross-functional teams.

Preferred Technical Skills

• Generative AI

• Machine Learning (ML)

• Deep Learning

• Natural Language Processing (NLP)

• Computer Vision

• Python

• MLOps

• CI/CD & CT Pipelines

• Vector Databases

• Retrieval-Augmented Generation (RAG)

• SQL

• Snowflake

• BigQuery

• AI Infrastructure & Cloud Platforms