Description

You will own the end-to-end deployment and scaling of machine learning models in production. You will operationalize complex algorithms, design data pipelines, and integrate ML use cases into business workflows.

Responsibilities

• Deploy and scale ML models, ensuring smooth handoffs with upstream and downstream teams.
• Design and maintain adaptable data pipelines for use-case-specific data requirements.
• Develop pipelines to generate and publish model performance metrics for Model Risk Oversight reviews.
• Operationalize standard ML algorithms (Regression, Classification) and NLP concepts (sentiment, topic modeling, TFIDF).
• Support production models and develop runbooks for ongoing maintenance.

Required Skills

• 8+ years of experience as an AI/ML Engineer.
• Strong proficiency in Python and SQL.
• Experience with GCP for model deployment and infrastructure.
• Working knowledge of scikit-learn, Pandas, PySpark, and vader sentiment.
• Understanding of NLP concepts including sentiment generation, topic modeling, and TFIDF.
• Proven experience in SDLC projects and collaborating with data science and engineering teams.
• Ability to design end-to-end deployment strategies for complex ML models.

Preferred Skills

• Experience with Model Risk Oversight frameworks and model review cadences.

Key Skills

Education

Any Graduate