Key Responsibilities

• Engage with client stakeholders to understand business requirements, model integration needs, and production goals.
• Design and maintain end-to-end ML pipelines, including training, validation, deployment, monitoring, and scaling.
• Deploy models into production using frameworks such as TFX, TorchServe, Flask, and Vertex AI.
• Lead MLOps efforts on Google Cloud Platform (GCP) with tools like Kubeflow, Vertex AI Pipelines, and CI/CD workflows.
• Ensure proper monitoring, logging, version control, and governance across deployed models.
• Drive improvements in ML workflows, including model performance optimization and re-training automation.
• Collaborate with data scientists, platform engineers, and DevOps to maintain system health, model traceability, and reproducibility.
• Communicate status, risks, and process improvements clearly to stakeholders and leadership.
• Guide and mentor junior team members in best practices for MLOps and scalable ML infrastructure.

Required Skills

• 5+ years of experience in AI/ML development, including hands-on experience building and deploying ML models.
• 3+ years of experience in MLOps, with strong hands-on skills in:
  • Python, PyTorch, TensorFlow, TFX
  • TorchServe, Flask
  • GCP (including Vertex AI, Cloud Functions, AI Platform)
  • Kubeflow, CI/CD tools for ML
• Experience with model monitoring, model registry, feature stores, and automated retraining pipelines.
• Excellent problem-solving, analytical, and communication skills.
• Proven ability to work independently while also collaborating effectively across teams.

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