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Metro location plus broad skillset and attractive ML role create moderate applicant competition.
Specialized MLOps, model deployment, and LLM experience create high domain specificity across industries.
Multiple explicit years requirements and mandatory ML/MLOps frameworks make shortlisting highly strict.
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Own the end-to-end machine learning production lifecycle including architecture, deployment, retraining, and performance monitoring of ML models in a scalable cloud environment.
Design, build, and automate CI/CD/CT pipelines for ML models ensuring reliability, compliance, model versioning, lineage tracking, and auditing.
Collaborate with cross-functional teams including data scientists, software engineers, and product managers to integrate ML solutions and provide architectural guidance and mentorship to related teams.
5+ years experience deploying and scaling ML solutions in cloud environments (AWS, GCP, Azure) using tools like SageMaker, Glue, Lambda, Docker.
7+ years programming experience in AI/ML languages such as Python or Scala.
4+ years experience developing deep learning and traditional ML models using frameworks like PyTorch, TensorFlow, HuggingFace, scikit-learn.
Degree in Computer Science, Engineering, or related field, or equivalent practical experience.
Experienced in building and automating ML pipelines with strong applied data science skills to select and evaluate machine learning models.
Demonstrated ability to maintain governance and compliance via model versioning, lineage, and auditing in production environments.
Strong capability to bridge data science and software engineering teams, mentoring others and simplifying complex ML concepts into clear technical documentation.