Description

Operationalize complex machine learning and generative AI models into production environments, ensuring end-to-end deployment, monitoring, and scalability.

Responsibilities

• Collaborate with data science and engineering teams to deploy, scale, and manage GenAI/LLM features including prompt engineering, evaluation metrics, and guardrails.
• Design and maintain adaptable data pipelines and model performance metric workflows to support Model Risk Oversight and compliance review cadences.
• Lead platform and DevOps initiatives, including CI/CD, containerization, observability, and environment automation within a major cloud provider (Google Cloud preferred).
• Integrate ML and GenAI use cases into business workflows, ensuring seamless data exchange with upstream and downstream systems.
• Develop runbooks and provide ongoing operational support to ensure model reliability and scalability.

Required Skills

• 10+ years of professional experience in machine learning engineering or related fields.
• Strong proficiency in Python and libraries such as scikit-learn, PySpark, pandas, and Hugging Face Transformers.
• Deep understanding of standard ML algorithms (Regression, Classification) and NLP concepts (sentiment analysis, topic modeling, TF-IDF).
• Hands-on experience with Generative AI techniques, including LLMs, prompt engineering, embeddings, and Retrieval Augmented Generation (RAG) using vector databases.
• Experience building and managing Q&A/assistant workflows and productionizing GenAI solutions.
• Proven track record of operationalizing complex models in production environments with robust monitoring.

Preferred Skills

• Working experience with Google Cloud Platform for ML infrastructure and deployment.
• Background in building reusable pipelines for model risk oversight and compliance.

Key Skills

Education

Any Graduate