Machine Learning Engineer

As an experienced Machine Learning Engineer with expertise in GenAI systems, your main responsibility will be to design and implement advanced ML solutions with a specific focus on LLM development, RAG systems, and optimization. Responsibilities: - Develop custom AI components and ML infrastructure for modular workflows - Implement LLM fine-tuning techniques including PEFT - Design model quantization and optimization strategies - Architect production-grade RAG systems with sophisticated retrieval - Create custom embedding solutions and hybrid search approaches - Develop prompt testing, evaluation, and management tools - Implement prompt security measures and few-shot learning approaches - Design vector clustering techniques and evaluation frameworks - Optimize LLM inference latency and implement efficient batching - Develop token usage monitoring systems and cost optimization - Create model deployment architectures for LLMs Required Qualifications: - Awareness and hands-on experience with LLM-based applications - 5+ years development experience with the ability to lead technical strategy - 3+ years ML experience with a proven ability to innovate new ML approaches - Master's degree in ML/AI or a related field - Expert-level programming skills in Python - Advanced expertise in ML frameworks (TensorFlow, PyTorch) - Proficiency in cloud platforms (AWS, Azure, GCP) - Expertise in GenAI architectures (RAG systems, vector search, prompt engineering) - Experience with vector databases and embedding technologies - Knowledge of AI governance, security, and cost optimization Nice to have: - Active contributor to GenAI community (publications, contributions to open-source projects, blog, personal GenAI projects, etc.) - Advanced certifications in LLM development (e.g., LangChain, Hugging Face, OpenAI) - Experience with multi-modal model development and deployment - Expertise in developing custom AI components for modular workflows - Published research or innovative contributions to ML/AI domains - Experience with model servers (vLLM, TGI),