2 Rlhf Jobs

Senior Technical Architect Machine Learning Solutions We are looking for a Senior Technical Architect with deep expertise in Machine Learning (ML), Artificial Intelligence (AI) , and scalable ML system design . This role will focus on leading the end-to-end architecture of advanced ML-driven platforms, delivering impactful, production-grade AI solutions across the enterprise. Key Responsibilities Lead the architecture and design of enterprise-grade ML platforms , including data pipelines, model training pipelines, model inference services, and monitoring frameworks. Architect and optimize ML lifecycle management systems (MLOps) to support scalable, reproducible, and secure deployment of ML models in production. Design and implement retrieval-augmented generation (RAG) systems, vector databases , semantic search , and LLM orchestration frameworks (e.g., LangChain, Autogen). Define and enforce best practices in model development, versioning, CI/CD pipelines , model drift detection, retraining, and rollback mechanisms. Build robust pipelines for data ingestion, preprocessing, feature engineering , and model training at scale , using batch and real-time streaming architectures. Architect multi-modal ML solutions involving NLP, computer vision, time-series, or structured data use cases. Collaborate with data scientists, ML engineers, DevOps, and product teams to convert research prototypes into scalable production services . Implement observability for ML models including custom metrics, performance monitoring, and explainability (XAI) tooling. Evaluate and integrate third-party LLMs (e.g., OpenAI, Claude, Cohere) or open-source models (e.g., LLaMA, Mistral) as part of intelligent application design. Create architectural blueprints and reference implementations for LLM APIs, model hosting, fine-tuning, and embedding pipelines . Guide the selection of compute frameworks (GPUs, TPUs), model serving frameworks (e.g., TorchServe, Triton, BentoML) , and scalable inference strategies (batch, real-time, streaming). Drive AI governance and responsible AI practices including auditability, compliance, bias mitigation, and data protection. Stay up to date on the latest developments in ML frameworks, foundation models, model compression, distillation, and efficient inference . 14. Ability to coach and lead technical teams , fostering growth, knowledge sharing, and technical excellence in AI/ML domains. Experience managing the technical roadmap for AI-powered products , documentations ensuring timely delivery, performance optimization, and stakeholder alignment. Required Qualifications Bachelors or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 8+ years of experience in software architecture , with 5+ years focused specifically on machine learning systems and 2 years in leading team. Proven expertise in designing and deploying ML systems at scale , across cloud and hybrid environments. Strong hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face, Scikit-learn). Experience with vector databases (e.g., FAISS, Pinecone, Weaviate, Qdrant) and embedding models (e.g., SBERT, OpenAI, Cohere). Demonstrated proficiency in MLOps tools and platforms : MLflow, Kubeflow, SageMaker, Vertex AI, DataBricks, Airflow, etc. In-depth knowledge of cloud AI/ML services on AWS, Azure, or GCP – including certification(s) in one or more platforms. Experience with containerization and orchestration (Docker, Kubernetes) for model packaging and deployment. Ability to design LLM-based systems , including hybrid models (open-source + proprietary), fine-tuning strategies, and prompt engineering. Solid understanding of security, compliance , and AI risk management in ML deployments. Preferred Skills Experience with AutoML , hyperparameter tuning, model selection, and experiment tracking. Knowledge of LLM tuning techniques : LoRA, PEFT, quantization, distillation, and RLHF. Knowledge of privacy-preserving ML techniques , federated learning, and homomorphic encryption Familiarity with zero-shot, few-shot learning , and retrieval-enhanced inference pipelines. Contributions to open-source ML tools or libraries. Experience deploying AI copilots, agents, or assistants using orchestration frameworks.

We are looking for a Senior Machine Learning Engineer with deep expertise in Transformers, Large Language Models (LLMs), and Natural Language Processing (NLP). This role involves designing, training, and fine-tuning state-of-the-art AI models for real-world applications. The ideal candidate will have a strong research background and hands-on experience in deploying scalable NLP solutions. Key Responsibilities Research, develop, and optimize Transformer-based architectures (e.g., BERT, GPT, T5, LLaMA) for various NLP tasks. Fine-tune LLMs on domain-specific datasets to improve accuracy and performance. Work on text generation, summarization, named entity recognition (NER), and semantic search applications. Implement and optimize embedding techniques for retrieval-augmented generation (RAG). Apply self-supervised and reinforcement learning techniques to enhance model performance. Deploy and scale ML models using cloud platforms (AWS, GCP, Azure) and containerized solutions like Docker and Kubernetes. Improve inference efficiency using quantization, distillation, and model optimization techniques. Collaborate with data engineers, software developers, and research scientists to integrate ML models into production. Stay updated with the latest advancements in AI, NLP, and Deep Learning, applying innovative techniques to solve business challenges. Required Skills & Qualifications Expertise in NLP & LLMs: Strong understanding of transformer-based models (e.g., BERT, GPT, T5, LLaMA). Programming Skills: Proficiency in Python and deep learning frameworks like PyTorch, TensorFlow, and Hugging Face Transformers. Model Optimization: Experience with quantization, pruning, and distillation to improve model efficiency. Data Handling: Strong experience in preprocessing, tokenization, and vectorization of large text datasets. Deployment & Scalability: Hands-on experience with MLOps, API development, cloud services (AWS, GCP, Azure), and containerization (Docker, Kubernetes). Information Retrieval & RAG: Knowledge of vector databases (FAISS, Pinecone, Weaviate) and embedding techniques. Mathematical Foundation: Strong background in linear algebra, probability, and deep learning architectures. Collaboration: Ability to work with cross-functional teams and communicate technical concepts effectively. Preferred Qualifications Experience in low-rank adaptation (LoRA) and fine-tuning LLMs with limited resources. Exposure to multimodal learning (text, images, audio). Research publications or contributions to open-source NLP projects. Familiarity with prompt engineering and fine-tuning for AI assistants. What We Offer Opportunity to work on cutting-edge AI and NLP projects with a talented team. Ability to shape the development of next-generation AI applications. Access to latest ML research, conferences, and learning resources. Flexible work arrangements (remote/hybrid options available). Competitive salary and performance-based incentives.