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Robert Half Logo Robert Half · IT - Software Development

Ai Lead Engineer

United States, Raleigh · Job Posted May 27, 2026
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Requirements

  • Proactive self-starter with excellent interpersonal, communication, and customer service skills
  • Expert-level AI/ML and full-stack development skills, with strong hands-on experience building and integrating backend services and frontend applications using modern frameworks such as Node.js and React. Strong emphasis on clean, maintainable, reproducible, well-tested, and well-documented code
  • Ability to manage multiple tasks and projects simultaneously
  • Collaborative team player with a focus on achieving common goals
  • Meticulous attention to detail
  • Quick learner with a passion for staying current with emerging technologies and industry trends
  • Deep expertise in RAG systems, LLMs, embeddings, vector databases, and AI infrastructure
  • Experience designing semantic retrieval and knowledge platforms, including curated corpora and grounding/citation patterns (e.g., “show your sources” for internal auditability)
  • Experience evaluating AI models for different tasks
  • Strong ability and experience to leverage cloud infrastructure
  • Experience with data quality and metadata management (data lineage, dataset versioning, business glossary/taxonomy) and implementing automated quality checks and anomaly detection
  • Experience implementing secure GenAI platform controls, including prompt logging, red-teaming, content filtering/leakage prevention, and model access controls/tenant isolation
  • Excellent collaboration skills and ability to work with non-technical stakeholders
  • Proven ability to work in existing codebases, improve reliability, performance, and readability over time
  • 5+ years of hands-on experience in machine learning engineering, with significant work developing and maintaining AI systems in production
  • Strong software engineering practices including modular design, refactoring, and technical debt management
  • unit, integration, and regression testing
  • as well as code reviews and shared coding standards
  • Strong expertise in machine learning fundamentals and statistical modeling, including, but not limited to: Supervised, unsupervised, and reinforcement learning
  • Model evaluation, bias, overfitting, and error analysis
  • Probabilistic and statistical reasoning
  • Proficiency in Python and major ML libraries (e.g., TensorFlow, PyTorch, scikit-learn, HuggingeFace, LangChain, LlamaIndex)
  • Hands-on experience with cloud-based ML platforms (AWS SageMaker, Azure ML, or Google AI Platform)
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