Artificial Intelligence Engineer

the Client•Reston, VA

3h•Hybrid

About The Position

The Artificial Intelligence Engineer will design, develop, and deploy AI/ML solutions that enhance the client's engineering, construction, and business processes. This role bridges advanced technology and practical application, ensuring AI systems deliver measurable value across projects and functions. The position requires strong technical expertise in AI and machine learning, combined with an understanding of engineering workflows and data governance.

Requirements

Proficiency in Python and AI frameworks (TensorFlow, PyTorch)
Experience with cloud platforms (Azure AI stack, Databricks)
Knowledge of MLOps, containerization (Docker/Kubernetes), and CI/CD pipelines
Strong problem-solving, communication, and collaboration skills
Works under minimal supervision with latitude for independent judgment
Handles issues of diverse scope within defined parameters
May act as a technical lead on small projects or initiatives
Bachelor's degree in Computer Science, Data Science, Engineering, or related field (required)
8–12 years of relevant experience in AI/ML development, or 12–15 years of related work experience without a degree

Nice To Haves

Familiarity with Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG)
Experience with knowledge graphs, agent-based AI systems, and predictive analytics
Understanding of engineering and construction workflows
Master's degree preferred

Responsibilities

AI Solution Development: Design, build, and implement AI/ML models for predictive analytics, generative design, and automation. Convert prototypes into production-ready applications using frameworks like TensorFlow, PyTorch, or Scikit-learn.
Data Engineering & Integration: Collaborate with data engineers to develop robust data pipelines and architectures. Apply ETL techniques and big-data tools to cleanse, organize, and transform data for AI models.
Deployment & Optimization: Deploy AI models on cloud platforms (Azure AI/ML, Databricks) and integrate with the client's digital ecosystem. Implement MLOps practices for lifecycle management, monitoring, and continuous improvement.
Collaboration & Stakeholder Engagement: Work with cross-functional teams (engineering, IT, project controls) to align AI solutions with business objectives. Communicate complex AI concepts to non-technical stakeholders.
Governance & Compliance: Ensure AI solutions adhere to the client's security, ethics, and compliance standards. Maintain documentation for models, processes, and decision-making.
Innovation & Continuous Learning: Stay current with emerging AI technologies (e.g., agentic AI, multimodal systems). Contribute to the client's AI Center of Excellence initiatives and innovation programs.