About the job 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.