Machine Learning Engineer

Role Overview

We are looking for a skilled Machine Learning Engineer with extensive experience to lead the creation of advanced, production-level AI systems. The primary focus involves working with Retrieval-Augmented Generation (RAG), natural language to SQL translation, and the fine-tuning of large language models. The ideal candidate will be responsible for developing, deploying, and managing robust ML solutions that support our software products.

Key Duties

• Architect and build production-quality RAG pipelines targeting knowledge-driven applications
• Create and enhance text-to-SQL engines that convert natural language queries into SQL commands
• Train, fine-tune, and adapt smaller language models for domain-specific use cases
• Perform fine-tuning on large language models utilizing techniques such as supervised fine-tuning, reinforcement learning with human feedback (RLHF), and parameter-efficient approaches
• Deploy and continuously improve ML models in live production, managing monitoring, version control, and performance tuning
• Optimize system metrics including model latency, operational cost, and accuracy in real-world scenarios
• Work collaboratively across teams to embed machine learning functionalities within product offerings
• Implement and uphold ML operations best practices, model validation standards, and deployment workflows
• Keep abreast of the latest advancements in large language models, RAG architectures, and machine learning tools

Professional Experience & Skills

• 4 to 5 years of hands-on experience in machine learning engineering roles
• Demonstrated success in building and deploying machine learning systems to production
• Familiarity with the complete machine learning lifecycle from data collection and processing to deployment and maintenance

Technical Expertise

• In-depth knowledge of RAG systems and their architectural implementation
• Experience working with vector databases including Pinecone, Weaviate, Chroma, FAISS, and semantic search embedding models
• Expertise in text-to-SQL frameworks, understanding of database schemas and query optimization, and proficiency in multiple SQL dialects
• Proficient in fine-tuning large language models such as GPT, Llama, and Mistral, including applying methods like LoRA, QLoRA, and prefix tuning
• Experience in training smaller language models from the ground up or customizing pre-existing ones
• Awareness of model quantization, distillation, and compression techniques to enhance efficiency
• Strong programming skills in Python and frameworks like PyTorch or TensorFlow
• Use of ML ops tools such as MLflow and Weights & Biases for deployment and monitoring
• Containerization experience with Docker and Kubernetes and cloud platform usage (AWS, GCP, Azure)
• API development experience using FastAPI or Flask
• Proficient in version control with Git and collaborative software development methodologies

Additional Qualifications

• Excellent analytical and problem-solving capabilities
• Effective communication skills to articulate complex technical information
• Experience working in agile environments
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related disciplines, or equivalent practical expertise

Preferred Attributes

• Familiarity with advanced prompt engineering techniques
• Knowledge of reinforcement learning from human feedback (RLHF)
• Experience with evaluation frameworks for language model applications
• Background in data annotation and synthetic dataset creation
• Contributions to open-source machine learning projects or related publications
• Experience conducting A/B testing and experimentation in production contexts
• Understanding of model safety, bias mitigation, and ethical AI practices