Sr. Machine Learning Engineer

Key Responsibilities

• 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.

Required Qualifications

• 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.

Preferred Qualifications

• PhD in Computer Science, Machine Learning, Engineering, or a related technical discipline.
• Experience with multi-agent RAG systems or AI agents coordinating workflows for advanced information retrieval.
• Familiarity with prompt engineering and building evaluation pipelines for generative models.
• Exposure to Snowflake or similar cloud data platforms.
• Broader data science experience, including forecasting, recommendation systems, or optimization models.
• Experience with streaming data pipelines, real-time inference, and distributed ML infrastructure.
• Contributions to open-source ML projects or research in applied AI/LLMs.
• Certifications in Azure, AWS, or GCP related to ML or data engineering.