AI Engineer

Job Qualifications for AI Engineer:

3+ years of software engineering, AI engineering, machine learning, data engineering, or related experience

Strong proficiency in Python.

Experience building AI-enabled applications using large language models (LLMs).

Experience working with APIs, cloud services, and application integration.

Familiarity with retrieval-augmented generation (RAG), semantic search, enterprise search, vector databases, or similar technologies

Understanding of prompt engineering and grounding techniques.

Experience processing structured and unstructured content, including documents, spreadsheets, reports, websites, PDFs, and databases.

Experience designing systems that leverage metadata, search, filtering, and knowledge retrieval.

Understanding of software development best practices including source control, testing, documentation, and deployment

Familiarity with SQL and database principles.

Preferred Qualifications

• Experience with Azure AI, Azure OpenAI, AWS AI services, Claude, OpenAI, or comparable AI platforms.

• Experience developing AI assistants, copilots, chat applications, or intelligent search solutions.

• Familiarity with machine learning workflows and model evaluation techniques

• Experience with document processing, knowledge management, or enterprise search platforms.

• Experience with Power BI, Tableau, or other visualization tools.

• Familiarity with cloud application architecture and secure enterprise environments.

• Experience working with large-scale data integration projects.

Responsibilities for AI Engineer

• Designing and developing AI-enabled applications and intelligent search solutions.

• Building and maintaining document ingestion, content processing, and knowledge management workflows.

• Implementing retrieval, search, filtering, and ranking capabilities

• Developing prompts, orchestration logic, and response frameworks for AI applications.

• Building metadata models and information structures that improve retrieval quality.

• Configuring AI systems to provide transparent, grounded, and reliable responses.

• Developing integrations between AI services, data repositories, cloud platforms, and business applications.

• Testing and validating AI outputs for quality, consistency, relevance, and usability.

• Troubleshooting retrieval, search, prompt, and performance issues