Machine Learning Engineer - USA (Remote)

Cogniify is hiring a Machine Learning Engineer to build and operate AI, LLM, and machine learning systems that support its products. The role owns shared capabilities such as training pipelines and evaluation frameworks, and develops AI-native applications and agent experiences from prototype through production.

Responsibilities:

• Design and own shared AI and machine learning components, frameworks, and pipelines for model training, evaluation, and fine-tuning that several product teams use
• Use ML fundamentals and LLM techniques such as prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG) to solve real customer problems, and measure how the models perform in production
• Prototype and build agent builder frameworks and AI experiences that sit inside the product, taking agentic AI ideas from early exploration to something ready for users
• Run fast prototypes and experiments to move from a proof of concept to accurate, well-performing production systems
• Design and build AI-native applications end to end, from backend services and APIs to LLM integrations in production

Requirements:

• A BS, MS, or higher in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience
• At least 5 years of experience building and deploying production AI and machine learning systems
• Strong computer science fundamentals including data structures, algorithms, and system design, along with solid machine learning foundations such as classification, regression, clustering, and neural networks
• Strong proficiency in Python and standard ML frameworks, including PyTorch, TensorFlow, Pandas, and NumPy
• Hands-on experience with Large Language Models (LLMs) and Generative AI, including prompt engineering, fine-tuning, RAG, and frameworks like LangChain
• Experience building or running agentic AI systems, agent frameworks, or long-term memory and context architectures for AI applications
• Familiarity with designing evaluation frameworks or offline and online evaluation pipelines (LLMOps / MLOps) to measure the quality of models and agents
• A track record of launching AI integrations in production and measuring their real-world impact
• Strong cross-functional collaboration skills, working well with data scientists, product managers, and software engineers across global teams
• Proficiency in one or more full-stack or backend languages such as JavaScript, TypeScript, or Java, and experience designing scalable distributed services, including microservices, relational and NoSQL data stores, and Kubernetes
• experience leading an engineering effort