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Metro location and broad ML/LLM plus MLOps skillset increase applicant competition.
ML, LLM and MLOps skills transferable, but enterprise/cloud integrations increase domain specificity.
Moderate technical requirements (ML, LLMs, MLOps, AWS) but no explicit years requirement.
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Design, develop, and deploy scalable AI and machine learning models including large language models and retrieval augmented generation pipelines in production environments.
Build and maintain data pipelines and AI microservices using Python, cloud native technologies (AWS, SageMaker, Docker, Kubernetes), and manage full AI model lifecycle including CI/CD, monitoring, and retraining.
Integrate AI systems with enterprise applications and ensure alignment with security, compliance, and responsible AI standards while collaborating with product and business teams on solution requirements.
Strong proficiency in Python with experience in NumPy and Pandas.
Experience in machine learning/deep learning frameworks such as PyTorch or TensorFlow and hands-on experience with large language models and generative AI.
Experience with cloud platforms like AWS, MLOps tools such as SageMaker, and container/orchestration technologies Docker and Kubernetes.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in end-to-end AI/ML solution delivery including model development, deployment, monitoring, and optimization in cloud environments.
Skilled in building AI microservices and integrating AI models with enterprise systems following REST API and microservices architecture.
Familiar with designing retrieval augmented generation architectures and knowledgeable on AI system security, compliance, and responsible AI practices.