Lead AI/ML Engineer - AI Agents

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Requirements:

• Strong proficiency in Python and modern ML frameworks such as PyTorch or TensorFlow, with experience developing and deploying AI systems.
• Deep understanding of core ML concepts, including transformers, generative models, and agent architectures such as MCP or A2A.
• Experience training large models on GPU clusters and integrating LLMs with external tools via APIs or orchestration frameworks.
• Comprehensive understanding of foundational deep learning, machine learning concepts, and state-of-the-art GenAI models.
• Hands-on experience training custom LLMs on GPUs and working with Retrieval-Augmented Generation (RAG) systems.
• Proven ability to build end-to-end ML pipelines—from data prep and experimentation to production deployment—in a cloud-native environment.
• A growth mindset, strong communication skills, and the ability to translate complex technical work to stakeholders across functions.
• Demonstrated experience working in R&D environments; publications in major AI/ML conferences are a plus, but not required.

Responsibilities:

• Architect and implement core infrastructure to support agent-based LLM systems, including multi-agent pipelines, RAG, and real-time orchestration.
• Train and fine-tune custom models, including LLMs and foundation models, to solve unique Upwork-specific challenges.
• Lead cross-functional collaboration across engineering, product, and research teams to align technical solutions with business impact.
• Mentor and guide engineers and researchers, helping foster a high-performing, inclusive team culture grounded in AI excellence.
• Translate experimental ideas into reliable, scalable production systems by applying best practices in AI engineering and deployment.
• Publish and share innovations where appropriate, contributing to Upwork’s visibility in the broader AI/ML research community.