Principal AI Engineer

Division: Engineering & Client Enablement
Contract: Full-Time
Location: Remote (Global)

About the Role

We are seeking a senior-level AI Engineer who will own the technical delivery of complex AI initiatives from concept through production. This role is highly execution-focused and suited for someone who enjoys solving ambiguous problems, building reliable systems, and engaging with clients to ensure successful outcomes. You will operate as a trusted technical leader, responsible for system architecture, implementation quality, and operational stability, leveraging a strong software engineering foundation combined with practical experience in delivering AI solutions in enterprise settings.

Main Responsibilities

• Design & Build AI Systems: Lead the creation of scalable AI platforms, including retrieval-augmented generation solutions and multi-step agent-based processes using modern orchestration frameworks.
• Engineering for Production: Develop robust, testable, and secure services ready for real-world use, supporting deployment workflows and contributing to operational best practices.
• Technical Advisory for Clients: Act as a hands-on technical advisor during client engagements, transforming loosely defined business needs into concrete system designs and implementation plans.
• Data & Knowledge Layer Development: Architect and implement data backends that combine structured databases, vector search engines, and graph-based knowledge systems for advanced AI reasoning.
• Cloud Execution: Deploy and operate AI workloads on leading cloud ecosystems such as AWS, Azure, or GCP, focusing on security, scalability, and cost efficiency.

Key Requirements

• Deep expertise in Python with an emphasis on maintainable architecture, object-oriented design, and async execution.
• Strong grasp of software architecture principles and system-level design.
• Experience with Docker and modern CI/CD practices.
• Hands-on work with agent-oriented frameworks including LangChain, LangGraph, or LlamaIndex.
• Practical experience optimizing LLM behavior through prompt strategies, context orchestration, and lightweight fine-tuning approaches.
• Solid understanding of core ML concepts such as embeddings, evaluation techniques, and performance trade-offs.
• Experience implementing vector-based retrieval using platforms like Pinecone or Qdrant.
• Working familiarity with graph databases (e.g., Neo4j) for modeling relationships and knowledge.
• Strong SQL skills and experience with relational data modeling.
• Proven experience deploying ML solutions using services such as SageMaker, Vertex AI, or Azure Machine Learning.

Nice to Have

• Familiarity with additional AI frameworks and libraries.
• Exposure to data security best practices.
• Experience in Agile methodologies.

Other Details

This position supports remote work arrangements and offers the opportunity to engage in innovative AI projects on a global scale.