We are seeking a skilled AI Engineer to design, build, and deploy our core AI agent systems. You will be instrumental in taking our ideas from research prototypes to robust, production-ready applications that deliver real value to our users. 

The successful candidate should be innovative, team-oriented, and have a strong ability to learn.

Responsibilities

• Design, develop, and evaluate robust AI agent systems and workflows using large language models [LLMs] and modern frameworks [e.g., LangChain, LangGraph].
• Own the full LLM application lifecycle: from prototyping and prompt engineering to deployment, monitoring, and maintenance in production.
• Architect and implement efficient Retrieval-Augmented Generation [RAG] systems utilizing vector databases.
• Collaborate with cross-functional teams to define requirements and translate business needs into technical solutions.
• Implement rigorous testing and evaluation frameworks to measure and improve the performance, reliability, and safety of our AI agents.
• Optimize AI systems for scalability, latency, and cost-effectiveness.

Required Qualifications

• Bachelor’s in Computer Science, Electrical Engineering, AI Engineering, or related field.
• At least 5 years of experience in machine learning or software engineeering with a focus on AI
• 1+ years of hands-on experience building applications with Large Language Models [LLMs]
• Strong proficiency in Python and experience with modern LLM frameworks [e.g., LangChain, LangGraph, CrewAI]
• Proven experience with the full ML Ops lifecycle: building, testing, deploying, and monitoring models in production [using tools like Docker, Kubernetes, MLflow, Weights & Biases].
• Solid understanding of vector databases and their application in RAG systems.
• Strong communication and collaboration skills.

Preferred Qualifications

• Experience with low-level ML frameworks [PyTorch, TensorFlow] for fine-tuning LLMs.
• Experience with speech recognition systems [e.g., Whisper] or computer vision models.
• Contributions to open-source AI projects.
• Experience with cloud platforms [AWS, GCP, Azure].
• Background in designing evaluation frameworks for AI systems.