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Remote access increases applicants but senior, niche ML/LLM requirements limit applicants.
Requires deep ML/LLM architecture and MLOps expertise, limiting cross-industry transferability.
Explicit 13+ years plus deep ML/LLM, MLOps and architecture requirements create strict filters.
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Lead end-to-end AI/ML solution design including architecture for NLP, machine vision, and AI use-cases using cloud infrastructure.
Translate business requirements into scalable, secure, and compliant AI architectures and technical designs, including model deployment pipelines and multi-agent systems.
Define non-functional requirements guidelines, conduct technical reviews, POCs, and mentor teams to implement best practices across AI/ML projects.
Total experience: 13+ years in AI/ML solution design and implementation including cloud and big data environments.
Proficiency in programming languages such as Python, Dotnet, Java, and experience with data libraries like Pandas and NumPy.
Bachelor’s or Master’s degree in Computer Science, Information Technology, or related field.
Experience with MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes), large scale system design, and database technologies (SQL, MySQL, Oracle).
Strong strategic ability to architect AI/ML systems integrating LLMs, generative AI frameworks, and prompt engineering for real-world business problems.
Experienced in overseeing scalable AI deployments balancing cost, latency, security/privacy, and compliance.
Capable of leading complex technical decisions and mentoring cross-functional teams through design and deployment phases.