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Tier-1 brand and metro location increase applicant density despite specialized MLOps requirements.
High domain specificity: MLOps and AI platform expertise transfers poorly outside ML-centric roles.
Multiple mandatory MLOps, infrastructure, and containerization requirements enforce strict technical filters.
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Design, build, deploy, and operate AI-enabled solutions delivering measurable business outcomes aligned with product goals.
Own end-to-end AI model lifecycle including versioning, release, monitoring, drift detection, and continuous improvement.
Build scalable infrastructure and automate deployments using Infrastructure-as-Code and CI/CD for AI workloads across environments.
Hands-on experience delivering AI/ML solutions into production environments.
Strong Python skills and experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
Experience with model serving, inference APIs, MLOps practices including CI/CD, monitoring, and drift detection.
Experience with containers (Docker, Kubernetes), automation tools (Jenkins, Git), Infrastructure-as-Code (Terraform, Ansible), and secrets management (Vault or equivalent).
Experienced in building and maintaining production AI/ML systems with strong operational ownership of model lifecycle management.
Proficient in infrastructure design and automation for scalable, robust AI deployments using modern DevOps and MLOps practices.
Collaborates effectively with data engineers and stakeholders to ensure data and model quality supporting product goals.