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Protocol Intelligence
Data-driven signals on your job's competitivenessRemote role and mid-level ML experience increase candidate competition despite specialized skill requirements.
Core ML skills transfer across industries, but regulated document workflow experience increases domain sensitivity.
Explicit 3+ years plus mandatory ML, cloud, MLOps, Docker/Kubernetes and production experience makes shortlisting strict.
Job Description
Structured overview of role & requirementsAbout This Role
Own end-to-end delivery of production AI and ML systems including experimentation, model training, fine-tuning, deployment, and continuous improvement.
Build and maintain scalable AI pipelines, APIs, and infrastructure focusing on performance, reliability, and cost optimization.
Implement MLOps practices including CI/CD, model monitoring, automated retraining, and collaborate with cross-functional teams to integrate AI into products.
Minimum Requirements
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
3+ years of experience as an AI Engineer, Applied AI Engineer, or similar role with hands-on expertise in training, fine-tuning, and production deployment of ML models.
Proficiency in Python and ML frameworks like PyTorch, TensorFlow, scikit-learn; experience with cloud platforms (AWS/GCP/Azure) and cloud ML services (SageMaker, Vertex AI).
Experience with MLOps practices including CI/CD pipelines, Docker, Kubernetes, API design, and production ML systems maintenance.
Ideal Candidate Profile
Experienced in building and scaling AI and LLM-powered applications with a strong focus on production readiness, model optimization, and system reliability.
Skilled in operating in cloud environments with practical knowledge of distributed system architecture and modern data infrastructure like PostgreSQL and vector databases.
Proactive in researching and applying emerging AI techniques and frameworks within regulated, security-conscious, and enterprise-focused contexts.