Catalant Technologies

Senior AI Engineer

Catalant Technologies

full-time

Posted on:

Origin:  • 🇺🇸 United States

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Job Level

Senior

Tech Stack

MySQLPythonPyTorch

About the role

  • Help drive adoption of AI on the Catalant platform and within our business, including:
  • AI Feature Development: Build and deploy enterprise-ready, AI-driven features that harness Catalant’s rich data to optimize our technology platform and internal workflows.
  • Building GenAI Systems With Cutting-Edge Design Patterns: Create and maintain systems that use technologies like advanced RAG, agents, and others to provide precise, contextually relevant information across our technology platform and internal processes.
  • Data Infrastructure: Team with data engineering to help ensure Catalant’s AI-focused data infrastructure is properly evolving, integrating multiple data sources into a robust, highly optimized data warehouse tailored specifically for AI applications.
  • Technical ownership over AI project lifecycles - own the projects you work on from planning to execution, with a team of friendly and helpful engineers that will provide assistance and feedback along the way.
  • Work cross-functionally by partnering with product, design, data analytics, business systems, etc. to plan and gather requirements for projects.
  • Deliver new product features to our clients quickly with a focus on accuracy, quality, usability, performance, and scalability
  • At least 8-years of professional software engineering experience, including 4+ years focused on generative AI and ML systems.
  • Hands-on experience bringing AI solutions to production in an enterprise setting (i.e., you don’t just write code in notebooks)
  • Integrating LLM APIs into responsive, real-time applications
  • Hands on, professional experience with common genAI design patterns, such as RAG, agentic systems, evals, model fine-tuning
  • Familiarity with a variety of genAI models and frameworks
  • Familiar with basic AI/ML concepts such as collection and utilization of testing/training data, validation and testing; basic ML algorithms; and an understanding of how LLMs work
  • Grounding in data science fundamentals, such as regression analysis, cross-validation, and techniques to address overfitting and underfitting
  • Experience in the full lifecycle of enterprise AI projects, including evaluation, guardrails, and monitoring, and maintaining AI systems in production.
  • Experience in MLOps / deploying models locally with GPUs and packages such as pytorch is a plus
  • Ability to work with stakeholders to understand requirements, collecting and incorporating feedback during and after deployment
  • Knowledge of OOP language(s); Deep and demonstrated python fluency required.
  • Deep working knowledge of one or more gen AI frameworks (such as Langgraph, LlamaIndex, or others)
  • Experience working with relational and vector databases; MySQL and Databricks experience a plus.
  • Demonstrated experience staying up to date with industry developments
  • Excitement about coaching peers in a constructive and positive way and ability to lead technically while also working independently and showing initiative to make an impact
  • Strong partnership with company leadership, engineering peers, product, and design during requirement discovery and solutioning
  • Ability to work with urgency, a strong sense of curiosity, and demonstrated ability to learn new technologies.

Requirements

  • At least 8-years of professional software engineering experience, including 4+ years focused on generative AI and ML systems.
  • Hands-on experience bringing AI solutions to production in an enterprise setting (i.e., you don’t just write code in notebooks)
  • Integrating LLM APIs into responsive, real-time applications
  • Hands on, professional experience with common genAI design patterns, such as RAG, agentic systems, evals, model fine-tuning
  • Familiarity with a variety of genAI models and frameworks
  • Familiar with basic AI/ML concepts such as collection and utilization of testing/training data, validation and testing; basic ML algorithms; and an understanding of how LLMs work
  • Grounding in data science fundamentals, such as regression analysis, cross-validation, and techniques to address overfitting and underfitting
  • Experience in the full lifecycle of enterprise AI projects, including evaluation, guardrails, and monitoring, and maintaining AI systems in production.
  • Experience in MLOps / deploying models locally with GPUs and packages such as pytorch is a plus
  • Ability to work with stakeholders to understand requirements, collecting and incorporating feedback during and after deployment
  • Knowledge of OOP language(s); Deep and demonstrated python fluency required.
  • Deep working knowledge of one or more gen AI frameworks (such as Langgraph, LlamaIndex, or others)
  • Experience working with relational and vector databases; MySQL and Databricks experience a plus.
  • Demonstrated experience staying up to date with industry developments
  • Excitement about coaching peers in a constructive and positive way and ability to lead technically while also working independently and showing initiative to make an impact
  • Strong partnership with company leadership, engineering peers, product, and design during requirement discovery and solutioning
  • Ability to work with urgency, a strong sense of curiosity, and demonstrated ability to learn new technologies.