Must Have Technical/Functional Skills:
- Programming Languages: Strong proficiency in Python or JavaScript. Experience with frameworks like LangChain, LlamaIndex, or CrewAI.
- ML & Data Tools: Solid understanding of vector databases, embeddings, and data pipelines.
- Cloud Platforms: Familiarity with deploying applications and microservices on AWS, Google Cloud Platform (Google Cloud Platform), or Microsoft Azure.
- Problem-Solving: Strong analytical skills with the ability to translate complex business requirements into effective AI-driven technical solutions
Roles & Responsibilities:
- System Integration: Integrate pre-trained AI models, LLMs, and computer vision tools into existing company software and legacy codebases. [1, 2, 3]
- Prompt & Pipeline Design: Develop, optimize, and evaluate prompt strategies, embedding models, and Retrieval-Augmented Generation (RAG) systems to improve response accuracy. [1, 2, 3, 4, 5]
- Infrastructure Management: Handle context windows, API latency, token costs, and API guardrails to build reliable production applications. [1]
- Model Deployment: Transform prototypes from data scientists into scalable APIs that integrate seamlessly with other applications. [1, 2]
- Performance Monitoring: Continuously monitor, troubleshoot, and debug AI system performance based on evolving business needs and user feedback. [1]
- Cross-functional Collaboration: Work closely with product managers, data scientists, and front-end engineers to implement AI-driven features