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Senior, niche MLOps/LLMOps role in metro with a recognized AI services brand creates moderate applicant competition.
Requires specialized MLOps and ML lifecycle expertise, moderately transferable across industries.
Explicit 8+ years plus many mandatory MLOps, AWS, and architecture requirements enforce strict shortlisting.
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Architect and implement enterprise-grade ML/LLM pipelines covering training, validation, deployment, versioning, monitoring, and CI/CD automation with an EKS-first container-oriented platform.
Lead MLOps strategy and execution for EVOKE Phase-2 programme ensuring alignment with project delivery roadmap and compliance with security, governance, and auditing requirements.
Serve as a technical authority across multiple projects by providing architectural patterns, best practices, troubleshooting, and mentoring related to cloud-native MLOps and LLMOps workflows.
7-14 years of experience with 8+ years in ML/AI engineering or MLOps roles with architecture exposure.
Strong expertise in AWS cloud-native ML stack including EKS, ECS, Lambda, API Gateway, CodeBuild/CodePipeline or equivalents.
Hands-on experience with major MLOps toolsets such as MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, or Seldon.
Deep understanding and practical experience in model lifecycle management, ML lifecycle stages, and security best practices including IAM and secrets management.
Experienced architect with proven ability to design scalable and secure MLOps and LLMOps platforms on AWS using EKS and SageMaker.
Strong collaborative approach working cross-functionally with data engineering, DevOps, product teams, and clients to deliver production-ready ML solutions.
Capable of providing technical leadership, guiding teams through design choices and technical uncertainty, and mentoring engineers on modern MLOps tools and practices.