Apply Now: Artificial Intelligence Engineer

We are seeking an experienced AI Developer (4–6 years) skilled in applying Large Language Models (LLMs) and building AI-driven applications to join our growing team. A significant part of this role involves designing and developing AI Agents within our platform with an initial focus on integrating external LLM APIs(e.g., OpenAI, Anthropic, Google) 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. Furthermore, as Ekyam.ai evolves, this role offers the potential to grow into customizing and deploying LLMs in-house, so adaptability and a strong foundation in ML/LLM principles are key.

Key Responsibilities

- AI Agent Development: 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.
- 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 the 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

- 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 (important for understanding models and future adaptation).
- Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes) for application deployment.
- Solid problem-solving skills and clear communication abilities.
- Experience working effectively in an agile environment.
- Willingness and capacity to learn and adapt towards future work involving deeper LLM customization and deployment.
- Bachelor's or Master's degree in Computer Science, AI, or a related field.
- Ability to work independently and collaborate effectively in a remote setting.

Preferred Qualifications

- Experience with frameworks like LangChain or LlamaIndex.
- Experience with observability and debugging tools for LLM applications, such as LangSmith.
- Experience with graph databases (e.g., Neo4j) and query languages (e.g., Cypher).
- Experience with MLOps practices, applicable to both current application monitoring and future model lifecycle management.
- Experience optimizing API call performance (latency/cost) or model inference.
- Knowledge of AI security considerations and bias mitigation.

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