AI Engineer
quest
Job Description
- Responsible for designing and implementing AI solutions that align with business objectives
- Design and implement production-ready applications using LLMs (GPT-4, Claude, Gemini) and other foundation models.
- Build and optimize RAG (Retrieval-Augmented Generation) systems using vector databases like Pinecone, Weaviate, or Qdrant.
- Implement model fine-tuning pipelines for domain-specific applications using techniques like LoRA and QLoRA.
- Create multi-agent systems and complex AI workflows using frameworks like Langraph or AutoGen.
- Optimize inference performance and manage API costs through intelligent caching and batching strategies
- Build evaluation frameworks to measure model performance, accuracy, and potential biases
- Integrate multiple AI models (text, vision, audio) to create multimodal applications
- Implement guardrails and safety measures to ensure responsible AI deployment
- Collaborate with product teams to translate user needs into AI-powered features
- Collaboration: Work with data scientists, engineers, and business stakeholders to deliver AI-driven solutions.
- Optimization & Evaluation: Continuously monitor and improve AI systems for accuracy and efficiency.
- Strategic Planning: Develop AI strategies and roadmaps aligned with organizational goals.
- Compliance & Ethics: Ensure AI solutions adhere to ethical standards and regulatory requirements (GDPR, fairness, bias mitigation).
- Leadership: Guide cross-functional teams and mentor junior engineers in AI best practices
- Security & Compliance: Data anonymization, secure model deployment, bias detection