Key skills
PythonGenerative AIMachine LearningMLOpsCloud PlatformsREST APIsDockerKubernetesDataRobotCommunication SkillsProblem-SolvingCAIArtificial IntelligenceMLGenAILarge Language Modelsscikit-learnPandasNumPyLangGraphFastAPIFlaskAWSAzureGCPK8sOAuthCI/CDCommunication
About this role
DataRobot delivers AI that maximizes impact and minimizes business risk. As an AI Engineer on our Professional Services team, you will work directly with strategic customers to design, build, and deploy AI solutions that address complex business challenges.
Responsibilities:
- Partner with Customers: Collaborate closely with customer stakeholders to understand their business goals, identify high-impact use cases, and define technical requirements for AI solutions
- Build & Deploy 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
- Serve as a Technical Expert: 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
- Deliver Value: Ensure that the solutions you build are robust, scalable, and directly contribute to the customer's business objectives
- Communicate & Collaborate: Clearly communicate complex technical concepts and project outcomes to both technical and non-technical audiences, from data scientists to C-level executives
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
- 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
- Familiarity with the DataRobot AI Platform is a strong plus