Artificial Intelligence Engineer

Position Overview

We’re looking for a skilled

AI Engineer

with 3 to 6 years of experience in building intelligent applications powered by

Large Language Models (LLMs) . This role centers around developing

AI Agents

for our platform, with a focus on integrating third-party LLM APIs (such as OpenAI, Anthropic, and Google) using advanced prompt engineering and

RAG (Retrieval-Augmented Generation)

techniques. You’ll be working primarily with

Python and FastAPI , designing logic that empowers these agents to handle complex workflows in the e-commerce and retail data space.

What You’ll Do

Develop AI Agents :

Architect, build, test, and maintain the core logic behind our AI agents within FastAPI-based services. Manage agent state, orchestrate tasks, integrate with platform data, and leverage LLM capabilities. LLM Integration & Prompt Design :

Work with APIs from major LLM providers, crafting and iterating on prompts tailored to retail use cases like summarization, Q&A, and content generation. Implement RAG Solutions :

Use vector databases (e.g., Pinecone, FAISS) to enrich LLM interactions with relevant context through Retrieval-Augmented Generation techniques. Build FastAPI Services :

Develop scalable FastAPI microservices to serve and manage agent logic, LLM interactions, and platform-level workflows within containerized environments (Docker, Kubernetes). Data Preparation :

Process and curate data to support prompt context, RAG pipelines, and potentially model fine-tuning in the future. Collaborate Across Teams :

Partner with product and engineering teams to deliver AI features and continuously adapt to advancements in the LLM ecosystem. Be ready to contribute to in-house model training and optimization efforts as we grow. What You Bring

3–6 years of hands-on software engineering experience, with a solid focus on AI or ML-based systems. Experience integrating external LLMs (OpenAI, Anthropic, Google, etc.) into real-world applications. Strong skills in prompt engineering and designing effective interactions with LLMs. Proficiency in

Python

and familiarity with

FastAPI

for building robust RESTful APIs. Practical knowledge in architecting AI-powered features, workflows, or autonomous agents. Hands-on experience with

RAG

implementations and

vector databases

like

Pinecone

or

FAISS . Foundational understanding of ML principles and exposure to tools like PyTorch, TensorFlow, or Hugging Face. Comfortable working with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes). Strong analytical mindset and communication skills. Familiar with Agile development methodologies and remote collaboration. Eager to grow in the AI space and contribute to future innovations in LLM training and deployment. Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related discipline.

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