Sr. AI Engineer

McCarthy is seeking a full-time Senior AI Engineer located in St. Louis, MO, who as part of the Enterprise Solutions and Analytics team will play a leading role in shaping and delivering AI capabilities through hands‑on engineering and technical mentorship. You will partner closely with the AI Architect and cross‑functional stakeholders to design, build, and scale production‑grade AI solutions across the enterprise.

This is a senior, hands‑on individual contributor role that combines advanced data engineering skills, strong data science and MLOps expertise, deep proficiency in generative AI development, and a commitment to responsible AI practices. You will own end‑to‑end solution delivery, from data preparation and model implementation through deployment, monitoring, and continuous improvement. You will also serve as a technical mentor and thought partner for other engineers.

You will design robust data and MLOps pipelines, develop and refine models, manage agent and prompt life cycles, and ensure every AI system is built with transparency, auditability, and governance in mind, in alignment with McCarthy’s AI architecture and standards.

• Lead the design, implementation, and optimization of scalable data and MLOps pipelines to support enterprise AI, advanced analytics, and agent‑based use cases as a senior individual contributor.
• Advance and mature data models and ontologies to serve as reusable building blocks for AI and analytics.
• Develop AI/ML solutions, including generative and agentic systems, using modern data science and ML engineering practices from experimentation through production deployment and continuous improvement.
• Leverage AI‑assisted development tools and coding copilots to accelerate prototyping, implementation, and iteration, while maintaining strong standards for code quality, testing, and security.
• Implement and champion MLOps and Prompt Ops best practices, ensuring models and agents are reproducible, monitored, observable, and auditable in production environments.
• Embed governance checkpoints and risk controls into AI development workflows, from data sourcing and feature engineering through deployment and retirement.
• Design, test, version, and operationalize prompts for generative AI systems; own the prompt lifecycle from creation and A/B testing through optimization and deprecation.
• Design Scope and design AI solutions with transparency, explainability, and human oversight built in, particularly for autonomous and semi‑autonomous (agentic) AI scenarios, following established architectural and governance guidelines.
• Collaborate with business and technical stakeholders to translate complex business problems into AI solutions that meet performance, security, and compliance requirements.
• Act as a technical mentor and coach for AI engineers:
• Provide guidance on solution design, coding practices, testing strategies, and performance optimization.
• Conduct code reviews and share patterns for high‑quality, maintainable, and secure AI solutions.
• Pair‑program and support skill development in data engineering, ML engineering, generative AI, and MLOps/Prompt Ops.
• Help drive consistent technical practices across teams by documenting reusable patterns, onboarding others to shared components, and reinforcing alignment with McCarthy’s AI architecture, standards, and Responsible AI practices defined by the AI Architect.

• 5–7+ years of experience in AI/ML engineering or data science, including delivering end‑to‑end production AI solutions with minimal supervision, in a senior individual contributor capacity.
• Demonstrated ability to define implementation approaches, make tradeoffs at the solution level, and lead execution of complex AI initiatives across teams.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field (or equivalent practical experience).
• Deep understanding of data science and ML principles:
• Feature analysis and engineering
• Model development and evaluation (supervised/unsupervised)
• Experiment design, offline/online testing, and performance tuning
• ETL/ELT pipelines and orchestration
• Distributed processing frameworks (e.g., Spark)
• Clou…