AI Engineer II

Responsibilities

• Design, develop, test, and maintain AI-powered applications, services, and workflows that support enterprise business processes.
• Build and support generative AI and agentic solutions using modern AI engineering practices and frameworks.
• Develop and maintain data pipelines, integrations, and reusable data products that enable AI and analytics use cases.
• Contribute to the design and evolution of data models and ontologies that support operational and analytical workflows.
• Develop and optimize prompts, retrieval strategies, and agent workflows to improve solution effectiveness, reliability, and user experience.
• Leverage AI-assisted development tools and coding copilots to accelerate delivery while maintaining high standards for code quality, testing, security, and maintainability.
• Participate in MLOps and PromptOps processes, including deployment, monitoring, evaluation, versioning, and continuous improvement of AI systems.
• Collaborate with business and technical stakeholders to translate business requirements into practical technical solutions.
• Support production AI solutions through troubleshooting, performance tuning, monitoring, and ongoing enhancement activities.
• Contribute to documentation, reusable patterns, and engineering best practices that improve team effectiveness and solution consistency.
• Follow established AI governance, security, and Responsible AI standards throughout the solution lifecycle.

Qualifications

• Minimum 3 years of experience in software engineering, AI engineering, machine learning, data engineering, or a related technical discipline.
• Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field (or equivalent practical experience).
• Experience building and deploying AI-enabled applications or machine learning solutions in production environments.
• Strong programming skills in Python and experience with modern software engineering practices.
• Data pipelines, ETL/ELT processes, and API-based integrations.
• SQL and relational data modeling.
• Cloud platforms such as Azure, AWS, or GCP.
• Containerized and serverless application deployment patterns.
• Building solutions on enterprise AI and data platforms such as Palantir Foundry, Azure AI, AWS, GCP, or Dataiku.
• Generative AI platforms and frameworks such as OpenAI, Anthropic, LangChain, LlamaIndex, Hugging Face, or similar technologies.
• Prompt engineering, evaluation methodologies, and retrieval-augmented generation (RAG) patterns.
• Experience with Palantir Foundry is strongly preferred, including familiarity with:
• Ontology-driven application development
• AIP and AI-enabled workflows
• Data products and pipeline development
• Workshop applications and operational workflows
• Code Repositories and software delivery within Foundry environments
• Understanding of responsible AI concepts, including transparency, human oversight, security, and governance practices.