AI Engineer

𝗪𝗵𝗼 𝗪𝗲 𝗔𝗿𝗲 𝗕𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗮𝗽𝗽𝗹𝘆, 𝘁𝗮𝗸𝗲 𝗮 𝗺𝗼𝗺𝗲𝗻𝘁 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗵𝗼𝘄 𝗖𝘆𝗢𝗽𝘀𝗣𝗮𝘁𝗵 𝘄𝗼𝗿𝗸𝘀. CyOpsPath is a NEW job directory built to make job searching faster and easier. We list time-sensitive opportunities from 10 different job boards in one place, helping job seekers cut through the noise and find relevant roles faster. Our listings span Remote, Hybrid, and On-Site positions across a wide range of industries and career levels. Candidates can follow a simple, consistent strategy — 5 applications a day for 30 days — to stay active and focused throughout their job search. 10 Job Boards. One Hub. More Opportunities. Less Searching. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀: You'll be redirected to the company's page to complete your application in a new window. Note: Job links expire once the maximum number of applications is received. Feel free to browse other listings. Key Responsibilities Model Development & Deployment: Design, train, fine-tune, and deploy machine learning models, Large Language Models (LLMs), and neural networks tailored to business needs. Architecture & Integration: Build robust APIs and microservices to integrate AI capabilities into web and cloud-based applications. Data Engineering: Design efficient data pipelines to collect, clean, preprocess, and analyze large datasets for training and validation. Optimization & Performance: Benchmark model efficiency, latency, and throughput; implement optimization techniques like quantization, pruning, and caching. Monitoring & Maintenance: Continuously monitor production AI systems for data drift, model decay, and system latency to ensure reliability and safety. Collaboration: Partner with product managers, software engineers, and domain experts to translate operational requirements into technical AI solutions. Qualifications & Requirements Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field (or equivalent practical experience). Programming Skills: Strong proficiency in Python and familiarity with backend technologies (e.g., FastAPI, Flask, or Node.js). AI/ML Frameworks: Hands-on experience with PyTorch, TensorFlow, Hugging Face, or OpenAI API ecosystem. Generative AI & LLMs: Proven experience with prompt engineering, Retrieval-Augmented Generation (RAG), vector databases (e.g., Pinecone, Chroma, Milvus), and fine-tuning open-source models. Cloud & DevOps: Familiarity with AWS, GCP, or Azure, along with containerization tools like Docker and Kubernetes. Software Engineering: Solid understanding of software design patterns, Git version control, CI/CD pipelines, and writing clean, maintainable code. Preferred Qualifications Experience deploying LLMs at scale in production environments. Knowledge of MLOps tools (e.g., MLflow, Weights & Biases, Kubeflow). Background in NLP, computer vision, or reinforcement learning. Show more Show less