
Expertise in AI Development
4 days ago
- Key Responsibilities:
- Design, develop, test, and maintain the core logic for AI Agents within FastAPI services. Orchestrate agent tasks, manage state, interact with platform data/workflows, and integrate LLM capabilities.
- Integrate with external LLM provider APIs. Design, implement, and rigorously test effective prompts for diverse retail-specific tasks (generation, Q&A, summarization).
- Implement and optimize Retrieval-Augmented Generation (RAG) patterns using vector databases to provide relevant context to LLM API calls made by agents.
- Build and maintain the scalable FastAPI microservices that host AI Agent logic and handle interactions with LLMs and other platform components in a containerized environment (Docker, Kubernetes).
- Prepare and preprocess data required for effective prompt context, RAG retrieval, and potentially for future fine-tuning tasks.
- Collaborate with cross-functional teams to deliver AI features and stay updated on LLM advancements.
- 4–6 years of hands-on experience in software development with a strong focus on AI/ML application development.
- Demonstrable experience integrating and utilizing external LLM APIs (e.g., OpenAI, Anthropic, Google) in applications.
- Proven experience with Prompt Engineering techniques.
- Strong Python programming skills.
- Practical experience building and deploying RESTful APIs using FastAPI.
- Experience designing and implementing application logic for AI-driven features or agents.
- Understanding and practical experience with RAG concepts and vector databases (Pinecone, FAISS, etc.).
- Solid understanding of core Machine Learning concepts and familiarity with frameworks like PyTorch, TensorFlow, or Hugging Face.
- Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes) for application deployment.
- Solid problem-solving skills and clear communication abilities.
- Ability to work independently and collaborate effectively in a remote setting.
- Experience with frameworks like LangChain or LlamaIndex.
- Experience with observability and debugging tools for LLM applications.
- Experience with graph databases and query languages.
- Experience optimizing API call performance or model inference.
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