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Remote, mid-level (3+ years) ML role with broad skill requirements increases applicant competition.
Requires specialized ML, LLM, and MLOps expertise making backgrounds less transferable.
Explicit 3+ years plus many mandatory ML, cloud, MLOps, and infra skills.
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Own end-to-end delivery of production AI/ML systems including training, fine-tuning, optimizing, and deploying models (including LLMs).
Build and maintain scalable training, data processing, inference pipelines, and APIs exposing AI capabilities.
Implement MLOps practices such as CI/CD, model monitoring, automated retraining, and collaborate with cross-functional teams to integrate AI into products.
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
3+ years of experience as AI Engineer, Machine Learning Engineer, or Applied AI Engineer.
Proficiency in Python, ML frameworks (PyTorch, TensorFlow, scikit-learn), and experience with production ML systems and cloud platforms (AWS, GCP, or Azure).
Experience with MLOps, CI/CD pipelines, Docker, Kubernetes; strong understanding of API design and distributed systems.
Experienced in operationalizing ML models with strong focus on scalability, reliability, and cost optimization in production.
Familiar with LLMs, Retrieval-Augmented Generation (RAG) systems, vector databases, and modern data infrastructure (PostgreSQL).
Able to own complex AI system delivery end-to-end in a startup/early-stage environment with cross-team collaboration.