Senior AI Engineer

About CyOpsPath

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We’re a curated job directory that pulls only time-sensitive, urgent-hire listings from the top 10 job boards — LinkedIn, Indeed, ZipRecruiter, and more — into one place.

Whether you’re actively job hunting or just keeping an eye out for the right opportunity, CyOpsPath cuts through the noise and surfaces what’s hiring right now.

Built for job seekers who don’t want to wait.

Key Responsibilities

• AI & ML Development: Architect, fine-tune, and deploy advanced machine learning models, deep learning architectures, and generative AI systems.
• Production Engineering: Build end-to-end AI pipelines for model training, testing, continuous integration, and real-time inference.
• Optimization & Performance: Optimize model efficiency, latency, and throughput for high-availability systems.
• Cross-Functional Collaboration: Partner with data engineers, product managers, and backend teams to define AI architecture and data requirements.
• Mentorship & Quality: Establish best practices for ML code quality, evaluation frameworks, and system monitoring.

Qualifications & Requirements

• Experience: 4+ years of hands-on experience in software engineering, with at least 2–3 years focused on building and deploying machine learning or AI models in production.
• Technical Skills: Proficiency in Python and modern AI/ML frameworks (e.g., PyTorch, TensorFlow).
• AI Specializations: Strong experience with LLMs, NLP, RAG architectures, computer vision, or predictive modeling.
• Cloud & MLOps: Hands-on expertise with cloud platforms (AWS, Azure, or GCP) and MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes).
• Data Systems: Solid understanding of data pipelines, SQL, vector databases (e.g., Pinecone, Milvus), and Big Data concepts.
• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related technical field.