Artificial Intelligence Engineer (Python)

Key Responsibilities:

Design, develop, and deploy autonomous AI agents using Python and relevant frameworks to address specific e-commerce challenges (e.g., personalized recommendations, intelligent search, customer service automation, dynamic pricing).
Build scalable microservices using Python and FastAPI to expose AI agent functionality and integrate with other systems.
Customize and fine-tune large language models (LLMs) like Llama, OpenAI’s GPT models, and Falcon for specific agent tasks, including text generation, conversational interactions, and process automation.
Implement and maintain robust and scalable APIs for AI agent interactions using Docker and Kubernetes.
Integrate AI solutions into web and cloud-based applications.
Perform data preprocessing, model training, and performance evaluation as needed for agent development.
Stay updated on the latest advancements in AI agent development, LLMs, and relevant technologies.
Collaborate with cross-functional teams (product, QA, backend) to define agent requirements, integrate agent capabilities, and deliver AI-driven features.
Troubleshoot and enhance agent performance, addressing issues related to accuracy, latency, and scalability.
Ensure compliance with AI ethics, security, and data privacy standards in the context of agent behavior.

Required Skills & Qualifications:

3–6 years of experience in software development with a strong focus on AI and Python. Demonstrated experience building and deploying AI-powered applications, preferably agents or systems with autonomous behavior.
Strong proficiency in Python, including experience with libraries relevant to agent development (e.g., LangChain, asyncio, concurrency).
Experience in fine-tuning large language models (LLMs) like Llama, OpenAI’s GPT models, and Falcon for specific tasks relevant to AI agents.
Solid understanding of RESTful API integration and cloud platforms (AWS, GCP, or Azure).
Skilled in NLP techniques, embeddings, and tokenization as applied to building conversational agents or other text-based AI agents.
Experience with vector databases (Pinecone, FAISS, or similar) and their application in building retrieval-augmented AI agents.
Proven ability to optimize AI systems for speed, accuracy, and scalability.
Experience deploying solutions with Docker and Kubernetes.
Strong problem-solving skills and experience with agile methodologies.

Preferred Qualifications:

Experience working with LangChain for building and orchestrating AI agents.
Experience with Retrieval-Augmented Generation (RAG) approaches in the context of AI agent development.
Knowledge of MLOps best practices for model lifecycle management.
Familiarity with AI security, bias mitigation, and advanced data privacy measures as they relate to the deployment and operation of AI agents.