AI Engineer
Skills
Computer ScienceAdoptionContinuous ImprovementDatabasesHealthcareMinutesModeling
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Bachelor's degree or equivalent professional experience in a quantitative field
- Hands-on experience building machine learning or AI-powered applications
- Experience with large language models prompt engineering embeddings vector databases retrieval-augmented generation or agentic workflows
- Strong Python proficiency with Pytest Pydantic and FastAPI
- Strong software engineering with production-grade practices including documentation testing version control code review CI/CD and observability
- Familiarity with AWS Azure or GCP and tools such as Claude Databricks Docker Gemini Kubernetes LangChain LangGraph OpenAI and SQL
- Working knowledge of data privacy security access controls and responsible AI considerations
- Ability to iterate quickly while maintaining high engineering standards
Nice to have
- Intellectual curiosity
- Practical mindset
- Regulated industry experience
- Reusable AI services mindset
- Strong point of view on model trade-offs
- Web data acquisition experience
Day to day
- Build and deploy intelligent systems that improve business operations customer experiences and product capabilities
- Integrate AI features into internal tools customer-facing products and operational workflows using APIs cloud services and modern engineering practices
- Design secure compliant and reliable AI solutions and evaluate model performance safety bias latency cost and business impact
- Produce documentation monitor systems and iterate on machine learning and generative AI solutions for continuous improvement
Hiring process
- Pre-screen coding exercise
- Phone interview with People Operations
- Experiential exercise interview with Data Science team
- Meetings with additional DnA team members
- Final interview with senior leadership