AI/ML Solutions Architect — MLOps & Cloud

About This Opportunity

CTI Staffing is partnering with a leading telecommunications organization to find an AI/ML Solutions Architect for their data science and engineering team, based onsite in the Dulles, VA area.

This team designs and productionizes machine learning solutions that run at enterprise scale. You'll be the technical authority on architecture — owning how AI/ML systems get designed, built, deployed, and maintained — while guiding engineering teams toward best practices. It's a hands-on leadership role for someone who has shipped real ML to production and wants to own the full lifecycle.

What You'll Do

• Lead the design and architecture of AI/ML solutions, ensuring scalability, reliability, and performance
  


• Partner with cross-functional teams to gather, analyze, and refine technical and business requirements
  


• Oversee prototyping, iteration, and productionization of DS/ML models, guiding engineers through best practices
  


• Architect end-to-end ML pipelines: data ingestion, feature engineering, training, evaluation, deployment, and MLOps
  


• Integrate AI/ML solutions with existing infrastructure across cloud platforms and modern data frameworks
  


• Validate and optimize deployed models to keep them aligned with business objectives and technical standards
  


• Provide technical leadership and mentorship on DS/ML architecture and implementation
  

Requirements

What You Bring

Must-Have:

• Bachelor's or Master's in Computer Science, Data Science, Engineering, or related field — or equivalent hands-on experience
  


• Proven track record architecting, building, and deploying ML models and AI systems in production environments
  


• Deep proficiency in Python and core ML libraries (scikit-learn, TensorFlow, PyTorch)
  


• Strong SQL and data engineering skills
  


• Experience with cloud platforms (Azure, AWS) and MLOps practices (CI/CD, model monitoring, automated retraining)
  


• Strong background in big data platforms (Apache Spark) and scalable data pipelines
  

Nice-to-Have:

• Experience with Databricks in a production setting
  


• Background mentoring or leading engineering teams
  


• Experience integrating ML into existing enterprise infrastructure
  


• Familiarity with feature stores and model registries
  

Technical Environment:

• Python, SQL
  


• scikit-learn, TensorFlow, PyTorch
  


• Azure, AWS
  


• Databricks, Apache Spark
  


• CI/CD and MLOps tooling
  

What Success Looks Like:

• Within 3–6 months, one or more ML pipelines architected and running reliably in production
  


• Engineering teams operating with clearer architectural standards and best practices
  


• Deployed models monitored, maintained, and aligned with business goals
  

Apply now or reach out to learn more: [email protected]