Senior Machine Learning Engineer, Enable

• 5+ years of experience in machine learning engineering, applied AI, or related fields
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering, or a related technical discipline
• Strong foundation in machine learning and data science fundamentals—including supervised/unsupervised learning, evaluation metrics, data preprocessing, and feature engineering
• Proven experience building and deploying RAG systems and/or LLM-powered applications in production environments
• Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers, or TensorFlow
• Experience with vector search tools (e.g., FAISS, Pinecone, Weaviate) and retrieval frameworks (e.g., LangChain, LlamaIndex)
• Hands-on experience with fine-tuning and distillation of large language models
• Comfortable with cloud platforms (Azure preferred), CI/CD tools, and containerization (Docker, Kubernetes)
• Experience with monitoring and maintaining ML systems in production, using tools like MLflow, Weights & Biases, or similar
• Strong communication skills and ability to work across disciplines with ML scientists, engineers, and stakeholders

Desirable

• We’re hiring a Senior Machine Learning Engineer to join our AI and Architecture team, contributing to the design, development, and deployment of cutting-edge machine learning systems
• In this role, you’ll work closely with ML scientists, data engineers, and product teams to help bring innovative solutions—such as retrieval-augmented generation (RAG) systems, multi-agent architectures, and AI agent workflows—into production
• As a Senior Machine Learning Engineer, you’ll play a key role in developing and integrating cutting-edge AI solutions—including LLMs and AI agents—into our products and operations at a leading SaaS company
• You’ll collaborate closely with product and engineering teams to deliver innovative, high-impact systems that push the boundaries of AI in rebate management
• This is a highly collaborative and fast-moving environment where your contributions will directly shape both the future of our platform and your own growth
• Design, build, and deploy RAG systems, including multi-agent and AI agent-based architectures for production use cases
• Contribute to model development processes including fine-tuning, parameter-efficient training (e.g., LoRA, PEFT), and distillation
• Build evaluation pipelines to benchmark LLM performance and continuously monitor production accuracy and relevance
• Work across the ML stack—from data preparation and model training to serving and observability—either independently or in collaboration with other specialists
• Optimize model pipelines for latency, scalability, and cost-efficiency, and support real-time and batch inference needs
• Collaborate with MLOps, DevOps, and data engineering teams to ensure reliable model deployment and system integration
• Stay informed on current research and emerging tools in LLMs, generative AI, and autonomous agents, and evaluate their practical applicability
• Participate in roadmap planning, design reviews, and documentation to ensure robust and maintainable systems