Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Niche Generative AI and MLOps focus reduces applicant density despite metro locations.
Strong generative AI and production ML requirements reduce cross-industry transferability.
Requires deep Generative AI, MLOps, vector DB and cloud experience, making filters strict.
Design, build, and productionize advanced ML/AI models focusing on Generative AI and Retrieval-Augmented Generation (RAG).
Lead end-to-end model lifecycle management including data prep, training, deployment, and monitoring ensuring scalable, reliable, and secure AI solutions.
Provide technical leadership and strategic partnership with stakeholders to drive AI innovation and operational excellence including MLOps pipeline optimization and AI governance.
7 to 9 years of work experience in data science or related AI domains.
Proven expertise in Generative AI technologies (LLMs, transformers, diffusion models) and RAG pipelines.
Hands-on experience with MLOps tools and practices such as CI/CD for ML, MLflow, Kubeflow, or Airflow.
Strong ML engineering skills in Python, PyTorch or TensorFlow, distributed training, and familiarity with cloud platforms like AWS, Azure, or GCP.
Experienced in leading AI projects from model development through deployment with emphasis on production readiness and monitoring.
Comfortable managing technical teams and collaborating cross-functionally to translate complex AI concepts to business impact.
Experienced in emerging AI technologies, AI governance, and implementing scalable, secure AI systems in enterprise environments.