Senior AI Engineer - Professional Services

DataRobot•Houston, TX

30d•Remote

About The Position

DataRobot delivers AI that maximizes impact and minimizes business risk. Our platform and applications integrate into core business processes so teams can develop, deliver, and govern AI at scale. DataRobot empowers practitioners to deliver predictive and generative AI, and enables leaders to secure their AI assets. Organizations worldwide rely on DataRobot for AI that makes sense for their business — today and in the future. As an AI Engineer on our Professional Services team, you will be at the forefront of the AI revolution, working directly with our most strategic customers. You'll be a trusted advisor and hands-on builder, translating complex business challenges into cutting-edge AI solutions that deliver tangible business value. This is a unique opportunity to design, build, and deploy a wide range of applications—from powerful predictive models to sophisticated Generative AI agents and chatbots. If you thrive on solving real-world problems and want to work with the latest in AI technology, this role is for you. This is a fully remote position with no requirement to go into an office on a regular basis. There will be travel requirements associated with this position to visit clients onsite up to 25% - 50% of the time.

Requirements

Strong proficiency in Python and common data science libraries (e.g., pandas, scikit-learn, NumPy, etc.).
Practical experience with Generative AI technologies, including Large Language Models (LLMs), vector databases.
Solid understanding of the end-to-end agentic AI lifecycle from building agents in frameworks like LangGraph or CrewAI, to at scale deployment and monitoring.
Demonstrable experience developing and deploying applications, including building REST APIs (e.g., using Flask, FastAPI) to serve ML models and GenAI logic.
Proficiency with containerization using Docker and experience deploying and managing applications on container orchestration platforms like Kubernetes (K8s).
Solid understanding of secure application development practices, including authentication/authorization (e.g., OAuth, API keys), secrets management, and securing public-facing endpoints.
Experience in a client-facing or consulting role with exceptional verbal and written communication skills. You must be comfortable leading technical discussions and presenting to diverse audiences.
A deep curiosity and a passion for solving complex, unstructured problems.
Approximately 6-8 years of hands-on experience in AI Application development, software engineering, machine learning engineering, or a similar role with a proven track record of deploying AI solutions or applications into production.
A Master’s Degree or Ph.D. in Computer Science, Statistics, Artificial Intelligence, Engineering, or a related quantitative field.
Hands-on experience with a major cloud platform (AWS, Azure, or GCP).
Understanding of MLOps principles and tools for model CI/CD, monitoring, and governance.

Nice To Haves

Familiarity with the DataRobot AI Platform is a strong plus.

Responsibilities

Collaborate closely with customer stakeholders to understand their business goals, identify high-impact use cases, and define technical requirements for AI solutions.
Design, develop, and deploy end-to-end AI solutions using the DataRobot platform and open-source tools. This includes: Agentic AI: Developing and deploying agents on DataRobot leveraging common frameworks such as Langgraph, CrewAI, Llama Index Generative AI: Building custom GenAI chatbots, Retrieval-Augmented Generation (RAG) systems. Predictive AI: Developing and deploying classic machine learning models for use cases like forecasting, churn prediction, and fraud detection.
Act as a subject matter expert on the DataRobot platform and modern AI/ML development, guiding customers on best practices for MLOps, model governance, and scaling AI initiatives.
Ensure that the solutions you build are robust, scalable, and directly contribute to the customer's business objectives.
Clearly communicate complex technical concepts and project outcomes to both technical and non-technical audiences, from data scientists to C-level executives.