IX Labs

We are seeking an experienced Machine Learning Engineer specializing in Large Language Models (LLMs), Natural Language Processing (NLP), and AI Agents. In this role, you will design, develop, and deploy cutting-edge language models and agent-based systems that drive innovation and deliver value across products and services.

Key Responsibilities

• Design, fine-tune, and optimize large language models (LLMs) for specific use cases and domains
• Develop and implement prompt engineering techniques to enhance model capabilities and performance
• Create autonomous AI agents capable of performing complex tasks through language understanding and reasoning
• Build retrieval-augmented generation (RAG) systems that combine LLMs with external knowledge sources
• Implement evaluation frameworks to benchmark LLM and agent performance across various metrics
• Optimize models for production deployment with attention to latency, throughput, and cost
• Design conversational flows and dialogue management systems for AI assistants
• Develop techniques to ensure responsible AI deployment, including safety guardrails and bias mitigation

• Stay current with the rapidly evolving NLP/LLM research landscape and implement state-of-the-art techniques

   

Requirements

• Strong experience with transformer-based models and frameworks (Hugging Face, PyTorch, TensorFlow)
• Practical experience fine-tuning, serving, and optimizing LLMs (e.g., GPT models, Llama, Claude, Mistral)
• Expertise in NLP techniques including prompt engineering, embeddings, and semantic search
• Experience with vector databases and retrieval systems (Pinecone, Weaviate, Faiss, etc.)
• Familiarity with model quantization, distillation, and other optimization techniques
• Experience with LLM orchestration frameworks (LangChain, LlamaIndex, etc.)
• Working knowledge of cloud platforms (AWS, GCP, Azure) for ML model deployment
• Proficiency with containerization technologies (Docker, Kubernetes)

• Strong understanding of LLM evaluation methodologies and metrics

   

Nice-to-Have skills:

• Experience developing multi-agent systems or agent-based architectures
• Knowledge of reinforcement learning from human feedback (RLHF) techniques
• Familiarity with LLM inference optimization (vLLM, TensorRT, ONNX)
• Experience with tools for LLM observability, evaluation, and debugging
• Experience with multimodal models combining text, vision, and audio
• Knowledge of LLM security, safety, and alignment techniques

Key Responsibilities

• Design, develop, and optimize machine learning models
• Collaborate with cross-functional teams (data scientists, product managers, software engineers) to integrate ML solutions into products
• Conduct experiments, analyze results, and implement improvements to enhance model performance
• Develop efficient data processing pipelines for model training and inference
• Optimize models for production deployment with attention to scalability and performance
• Create and maintain documentation for models, datasets, and processes
• Monitor model performance in production and implement updates to ensure reliability and quality
• Stay current with ML research and evaluate new techniques for potential implementation
• Participate in code reviews and contribute to best practices in ML engineering

Requirements

• Strong experience with ML frameworks and libraries such as PyTorch, scikit-learn, spaCy, and Hugging Face
• Practical understanding of the mathematics behind modern machine learning, linear algebra, and statistics
• Experience with cloud platforms (AWS, GCP, Azure) for ML deployment
• Familiarity with containerization technologies (Docker, Kubernetes)
• Experience with version control systems (Git) and CI/CD pipelines
• Proficiency with SQL and experience working with large datasets
• Excellent problem-solving and analytical thinking skills
• Strong communication skills with ability to explain complex technical concepts to non-technical stakeholders

Nice-to-Have skills:

• Experience with model versioning and experiment tracking tools (MLflow, DVC, Weights & Biases)
• Knowledge of data visualization tools (Matplotlib, Seaborn, Plotly)
• Experience with feature stores and ML platforms
• Understanding of ML monitoring and observability best practices