AI Architect

SWITS DIGITAL Private Limited; Washington (District of Columbia); On-site; USD 130,000 - 160,000; Full time

Job summary

A tech solutions company based in Washington, D.C, is seeking an experienced AI Architect to lead the design and implementation of AI systems. The ideal candidate will have over 8 years of experience in software engineering, focusing on AI/ML, with strong communication skills to bridge technical and non-technical stakeholders. Responsibilities include transitioning AI projects to enterprise systems, selecting AI technologies, and ensuring ethical usage. This role presents a significant opportunity to shape enterprise AI strategies and optimize costs associated with AI workloads.

Qualifications

• 8–10+ years in software engineering or data architecture, including 4+ years in AI/ML.
• Proven ability to manage and optimize computing costs for heavy AI workloads.
• Exceptional communication skills for non-technical stakeholders.

Responsibilities

• Lead transition of AI projects from PoCs to enterprise-wide systems.
• Evaluate and select AI technologies based on business needs.
• Design end-to-end AI pipelines for model deployment and monitoring.
• Implement robust MLOps practices for continuous model training.
• Collaborate with data engineers to design advanced AI data architecture.
• Establish ethical guardrails for AI usage and compliance.

Skills

AI/ML systems design and deployment

Cost Management

Communication & Leadership

Database Knowledge

Tools

SQL

Snowflake

BigQuery

Job description

Location: Washington, D.C

Role

AI Architect

Qualifications

• Experience Level: 8–10+ years in software engineering or data architecture, with at least 4+ years specifically dedicated to AI/ML systems design and deployment in production environments.
• Cost Management: Proven ability to manage and optimize the computing costs associated with running heavy AI workloads (e.g., token optimization, GPU allocation).
• Communication & Leadership: Exceptional ability to translate complex AI capabilities and limitations to C-suite executives and non-technical stakeholders.
• Database Knowledge: Familiarity with SQL and data warehousing concepts (e.g., Snowflake, BigQuery) for data pipeline orchestration.

Essential Job Functions and Required Skills

• Enterprise AI Strategy: Lead the transition of AI projects from localized Proof of Concepts (PoCs) to scalable, enterprise-wide production systems.
• Technology Selection: Evaluate and select the appropriate AI technologies (e.g., Deep Learning, Natural Language Processing, Computer Vision, Generative AI) based on business requirements, cost, latency, and data privacy constraints.
• Architecture Design: Architect end-to-end AI pipelines, including data ingestion, model training/fine-tuning, deployment, and monitoring.
• MLOps & Infrastructure: Design and implement robust MLOps practices for continuous integration, continuous deployment (CI/CD), and continuous training (CT) of AI models to prevent model drift and degradation.
• Data & Integration: Work closely with data engineers to design the data architecture required for advanced AI, including vector databases and complex RAG workflows.
• AI Governance & Ethics: Establish guardrails for AI usage, ensuring models are fair, transparent, secure against adversarial attacks, and compliant with data privacy regulations (e.g., GDPR, CCPA).