
AI Engineer – Prompt Engineering
3 weeks ago
Core ResponsibilitiesPrompt Engineering & AI Development
- Design and implement BAML prompts based on technical briefs and business requirements.
- Engineer optimal context windows through advanced knowledge retrieval and context engineering techniques.
- Create and maintain comprehensive evaluation frameworks using Evals to optimize prompt performance.
- Implement feedback loops for continuous prompt improvement and monitoring.
- Leverage LangChain, LangGraph, or similar AI orchestration frameworks to build modular AI workflows.
- Apply evals tools such as Galileo (or similar) for systematic testing, monitoring, and improving prompt quality.
Backend Development & Deployment
- Build and maintain scalable APIs using FastAPI or similar frameworks for AI model deployment.
- Design and implement database architectures (SQL/NoSQL) for AI application data storage.
- Develop robust data pipelines for real-time AI inference and processing.
- Handle server-side logic, authentication, and security implementations.
Nice to Have
- Deploy AI applications using containerization (Docker).
- Set up CI/CD pipelines for automated testing and deployment of AI systems.
- Manage cloud infrastructure (AWS, Azure, GCP) for scalable AI applications.
- Monitor application performance and implement optimization strategies.
Technical Requirements
- Advanced Python programming with focus on clean, scalable code.
- Experience with version control systems (Git) and collaborative development.
- Understanding of security best practices and API authentication.
- Familiarity with async programming and performance optimization.
What You’ll Achieve
- Collaborate with technical leads to architect and deploy end-to-end AI applications.
- Build scalable, production-ready systems that serve AI models efficiently.
- Contribute to our technical architecture and best practices for AI development.
Ideal Candidate
You’re a seasoned engineer who can seamlessly bridge the gap between AI innovation and production deployment. You understand that great AI products require both expertly crafted prompts (including BAML, Galileo evals, and context engineering) and robust, scalable backend systems. You’re comfortable taking a business requirement and delivering a complete, deployed solution that performs reliably at scale.
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