AI Application Developer

AI Application Developer

We are seeking an experienced professional to join our team as an AI Application Developer. The successful candidate will be skilled in applying Large Language Models (LLMs) and building AI-driven applications.

The primary focus of this role involves designing and developing AI Agents within our platform with an initial emphasis on integrating external LLM APIs via sophisticated prompt engineering and RAG techniques into these agents, built using Python + FastAPI.

You will architect the logic for these agents, enabling them to perform complex tasks within our e-commerce and retail data orchestration pipelines.

• Ai Agent Development: Design, develop, test, and maintain the core logic for AI Agents within FastAPI services.
• LLM API Integration & Prompt Engineering: Integrate with external LLM provider APIs, design, implement, and rigorously test effective prompts for diverse retail-specific tasks (generation, Q&A, summarization).
• RAG Implementation: Implement and optimize Retrieval-Augmented Generation (RAG) patterns using vector databases to provide relevant context to LLM API calls made by agents.
• FastAPI Microservice Development: Build and maintain scalable FastAPI microservices that host AI Agent logic and handle interactions with LLMs and other platform components in a containerized environment (Docker, Kubernetes).
• Data Processing for Ai: Prepare and preprocess data required for effective prompt context, RAG retrieval, and potentially for future fine-tuning tasks.
• Collaboration & Future Adaptation: Work with cross-functional teams to deliver AI features, stay updated on LLM advancements, and be prepared to learn and contribute to potential future in-house LLM fine-tuning and deployment efforts.

Required Skills & Qualifications

• Experience: 4–6 years of hands-on experience in software development with a strong focus on AI/ML application development.
• LLM Integration: Demonstrable experience integrating and utilizing external LLM APIs (e.g., OpenAI, Anthropic, Google) in applications.
• Prompt Engineering: Proven experience with Prompt Engineering techniques.
• Python Programming: Strong Python programming skills.
• RESTful API: Practical experience building and deploying RESTful APIs using FastAPI.
• Ai Driven Features: Experience designing and implementing application logic for AI-driven features or agents.
• RAG Concepts: Understanding and practical experience with RAG concepts and vector databases (Pinecone, FAISS, etc.).
• Ml Frameworks: Solid understanding of core Machine Learning concepts and familiarity with frameworks like PyTorch, TensorFlow, or Hugging Face.
• Cloud Platforms: Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes) for application deployment.
• Problem Solving: Solid problem-solving skills and clear communication abilities.
• Agile Environment: Experience working effectively in an agile environment.
• Adaptability: Willingness and capacity to learn and adapt towards future work involving deeper LLM customization and deployment.
• Education: Bachelor's or Master's degree in Computer Science, AI, or a related field.
• Collaboration: Ability to work independently and collaborate effectively in a remote setting.

Preferred Qualifications

• Frameworks: Experience with frameworks like LangChain or LlamaIndex.
• Observability: Experience with observability and debugging tools for LLM applications.
• Graph Databases: Experience with graph databases (e.g., Neo4j) and query languages (e.g., Cypher).
• MLOps Practices: Experience with MLOps practices, applicable to both current application monitoring and future model lifecycle management.
• Api Optimization: Experience optimizing API call performance (latency/cost) or model inference.
• Ai Security: Knowledge of AI security considerations and bias mitigation.