AI Architect

Position Summary:

Title: Data Scientist Premium III AI Architect Duration: 6 Month - Long Term
Location: Washington, DC 20043
Work Model: 4 days per week from Day 1, with a full transition to 100% onsite anticipated soon

Scope of work
The scope of work includes to design, build, and govern scalable, secure, and cost-effective AI solutions across the enterprise. This role bridges the gap between complex AI research and practical software engineering, orchestrating everything from traditional predictive models to advanced Generative AI and Retrieval-Augmented Generation (RAG) systems.

SKILLS / EXPERIENCE REQUIRED

Senior AI Architect should have the following skills sets/experience.

• 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 & 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 Architecture Design 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).
  
  Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.