AI Engineer

AI Engineer

We are looking for an AI Engineer ready to push the boundaries of what's possible with LLMs in a high-paced, intellectually demanding environment. This is an opportunity to shape the intelligence layer of our cutting-edge AI platform—designing, building, and optimizing the systems that power enterprise-grade AI experiences. You'll have the autonomy to drive ideas from concept to production, working at the intersection of applied research and scalable engineering.

Key Responsibilities

• LLM Development & Integration: Design, build, and optimize LLM-powered features using known frameworks.
• AI System Architecture: Work on robust, scalable AI pipelines that handle retrieval, reasoning, and generation at enterprise scale.
• Prompt Engineering & Evaluation: Design advanced prompting strategies and build comprehensive evaluation pipelines—defining metrics, curating test sets, and running systematic benchmarks to ensure consistent, high-quality model outputs for domain-specific use cases.
• Backend Development: Build performant Python-based backend systems that seamlessly integrate AI capabilities with our core product infrastructure.
• Data & Vector Systems: Work with vector databases and data pipelines to power knowledge systems.
• Cross-Functional Collaboration: Partner with product, design, and engineering teams to translate AI capabilities into intuitive user experiences.

Qualifications

• Autonomous & Ideas-Driven: Self-directed with a bias toward action—you identify problems and propose solutions.
• Agent Architectures: Experience with agent architectures, tool-use patterns, and multi-step reasoning systems.
• Proficiency in Python: Deep expertise in Python with a strong grasp of async programming, type systems (Pydantic), and best practices for production-grade code.
• Scalable Backend Systems: Proven ability to design and build backend systems that are performant, reliable, and maintainable.
• Database Expertise: Strong experience with PostgreSQL and familiarity with vector databases for semantic search and retrieval.
• Strong Problem-Solving Skills: Ability to debug complex systems, optimize for latency and cost, and navigate ambiguity.
• Proven Track Record: Demonstrable experience through previous roles, side projects, or open-source contributions in the AI/ML space.

Bonus Points

• Fundamentals of statistics, machine learning, and deep learning.
• Experience with evaluation frameworks and observability tools.
• Contributions to open-source AI projects or published research.
• Familiarity with MLOps, model serving (vLLM, TGI), and infrastructure for AI at scale.
• Background in fine-tuning, RLHF, or working with open-weight models.