AI/ML Engineer

Responsibilities:

Generative AI Development:

• Design, develop, and fine-tune Generative AI solutions using models like Google's Gemini for tasks such as information extraction, document summarization, and report generation.
• Architect and implement advanced Retrieval-Augmented Generation (RAG) systems to enhance model accuracy and provide verifiable, context-aware responses.
• Research and apply emerging GenAI techniques, such as agentic frameworks, to build more autonomous and capable systems.

End-to-End Machine Learning:

• Design and deploy a wide range of ML models (classification, regression, forecasting, etc.) on Google Cloud Platform.
• Build and maintain robust, automated MLOps pipelines for data preprocessing, feature engineering, model training, validation, and deployment using tools like Vertex AI, BigQuery. etc.
• Conduct deep data analysis to uncover insights, validate hypotheses, and guide feature engineering for improved model performance.

Collaboration And Strategy:

• Partner closely with data scientists, software engineers, and other business stakeholders to frame problem statements, define technical requirements and deliver integrated AI/ML solutions.
• Champion best practices in software engineering and MLOps to ensure the quality, maintainability, and scalability of our machine learning systems.
• Continuously evaluate and stay current with the latest advancements in the ML and GenAI landscape.

Required Qualifications:

• Experience: 3+ years of professional experience building and deploying machine learning models in a production environment.
• Education: Bachelor's degree in Computer Science, Data Science, Statistics, or a related quantitative field.
• Programming: Advanced proficiency in Python and its core data science/ML libraries (e.g., PyTorch, scikit-learn, Pandas).
• Data & SQL: Advanced proficiency in SQL for complex data manipulation, aggregation, and analysis.
• Generative AI: Demonstrable, hands-on experience in prompt engineering and/or fine-tuning Large Language Models (e.g., Gemini).
• Cloud Platform: Hands-on experience with a major cloud provider, with a strong preference for Google Cloud Platform (GCP).
• MLOps: Solid understanding of MLOps principles and experience with related tools (e.g., Vertex AI, CI/CD).

Preferred Qualifications (Nice-to-Haves):

• Master’s or PhD in a relevant field.
• Specific experience with GCP services like Vertex AI, BigQuery, Google Cloud Storage, and GKE.
• Experience building RAG systems from the ground up.
• Proven ability to lead technical projects and mentor other engineers.