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Remote senior MLOps role with broad LLM and cloud requirements creates moderate applicant competition.
MLOps and LLM specialization limits transferability, requiring ML-focused background.
Explicit 6+ years plus 3+ years production MLOps and cloud/LLM requirements impose high selectivity.
Own the end-to-end production lifecycle of ML and AI capabilities including deployment pipelines, monitoring, retraining, and governance.
Operate and maintain reliability, observability, security, and cost-effectiveness of both traditional ML and generative AI/LLM workloads.
Collaborate closely with US-based data science teams, taking significant operational ownership during India work hours for model production environments.
6+ years total software, data, or ML engineering experience with at least 3+ years deploying and operating AI/ML systems in production.
Strong experience with both traditional machine learning and large language model (LLM) workloads in production environments.
Proficient with cloud platforms (AWS preferred), ML CI/CD, containerization, infrastructure as code, and Python programming.
Bachelor's degree in Computer Science, Engineering or related technical field, or equivalent practical experience.
Experienced senior engineer comfortable owning complex production ML/AI infrastructure with strong operational troubleshooting and incident management skills.
Expertise in generative AI/LLM systems including operational controls, monitoring token usage, cost management, and governance practices.
Effective collaborator across distributed US-India teams with proven ability to establish repeatable standards and automate manual ML deployment operations.