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๐๐ฒ๐ณ๐ผ๐ฟ๐ฒ ๐๐ผ๐ ๐ฎ๐ฝ๐ฝ๐น๐, ๐๐ฎ๐ธ๐ฒ ๐ฎ ๐บ๐ผ๐บ๐ฒ๐ป๐ ๐๐ผ ๐๐ป๐ฑ๐ฒ๐ฟ๐๐๐ฎ๐ป๐ฑ ๐ต๐ผ๐ ๐๐๐ข๐ฝ๐๐ฃ๐ฎ๐๐ต ๐๐ผ๐ฟ๐ธ๐.
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 โ 5 applications a day for 30 days โ to stay active and focused throughout their job search.
10 Job Boards. One Hub. More Opportunities. Less Searching.
๐๐ฝ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ป๐๐๐ฟ๐๐ฐ๐๐ถ๐ผ๐ป๐: 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: Bachelorโs or Masterโs 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.