For one of our client: We’re looking for an AI Data Engineering Lead to help shape and execute our AI/ML vision. In this role, you’ll lead the development of intelligent, production-ready AI systems, focusing on building scalable data infrastructure to support LLMs, agentic frameworks, and multi-agent systems.

You’ll work at the intersection of data engineering, AI research, and cloud architecture, bringing deep technical expertise and strategic insight to a fast-moving team. This is a hands-on development and leadership role where you'll drive system design, write high-quality code, and grow a world-class AI engineering function — all while building on the Google Cloud Platform (GCP) stack.

Fully REMOTE role

What You’ll Do

• Architect Next-Gen AI Infrastructure: Design and implement scalable, AI-first data platforms using GCP tools like Vertex AI, BigQuery, Dataflow, and Pub/Sub

Build Streaming Pipelines: Develop real-time data pipelines that capture user signals and fuel ML-driven systems and agentic behaviors

• Lead End-to-End AI System Development: Ship production-grade AI/LLM applications with LangChain, HuggingFace, AutoGen, and similar frameworks
• Own the ML Lifecycle: Build and maintain MLOps pipelines with CI/CD, testing, observability, and model evaluation — especially focused on LLM performance and reliability
• Evaluate AI at Scale: Develop internal tools and metrics to monitor safety, performance, and overall quality of generative AI applications
• Be a Technical Leader: Write clean, production-ready Python code, drive architectural decisions, and champion engineering best practices
• Partner Cross-Functionally: Collaborate closely with Product and AI/ML leadership to align roadmap and execution with strategic goals
• Grow the Team: Mentor engineers, support hiring, and foster a high-performance AI engineering culture

What We’re Looking For

• 5+ years of experience in software, data, or ML engineering
• Expertise in Python, along with strong hands-on experience with GCP services (Vertex AI, Dataflow, BigQuery) and scalable data/ML pipelines
• Solid understanding of CI/CD, observability, and monitoring practices in production ML environments
• Proven experience deploying LLM-powered applications, including agentic workflows and multi-agent systems
• Familiarity with LangChain, LlamaIndex, HuggingFace, and prompt engineering for LLMs

Nice to Have

• Master’s or PhD in Computer Science, AI, ML, or related fields
• Experience working with AWS, Azure, or other cloud ML stacks
• Proficiency with graph databases (e.g., Neo4j) and a strong grasp of SQL/NoSQL
• Exposure to big data frameworks like Apache Spark or Flink
• Contributions to open-source AI/ML projects
• Awareness of Responsible AI, data governance, and model ethics

If this sounds like you - Apply now!

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