Job Overview:
Requirement/Must Have:
Advanced proficiency in Python.
Strong expertise in data analysis and SQL.
Hands-on experience in machine learning model development and deployment.
Demonstrable experience in Generative AI, including prompt engineering and/or fine-tuning Large Language Models such as Gemini.
Experience with cloud platforms, preferably Google Cloud Platform (GCP).
Solid understanding of MLOps principles and related tools.
Experience:
5+ years of professional experience building and deploying machine learning models in a production environment.
Hands-on experience designing and deploying ML models such as classification, regression, and forecasting models.
Experience building and implementing Retrieval-Augmented Generation (RAG) systems.
Experience working with GCP services such as Vertex AI, BigQuery, Google Cloud Storage, and GKE.
Proven ability to lead technical projects and mentor other engineers.
Responsibilities:
Design, develop, and fine-tune Generative AI solutions using models such as Gemini for information extraction, document summarization, and report generation.
Architect and implement advanced Retrieval-Augmented Generation (RAG) systems to improve model accuracy and deliver context-aware, verifiable responses.
Research and apply emerging Generative AI techniques, including agentic frameworks, to build more autonomous and capable systems.
Design and deploy a wide range of machine learning models on Google Cloud Platform.
Build and maintain robust, automated MLOps pipelines for data preprocessing, feature engineering, model training, validation, and deployment using tools such as Vertex AI and BigQuery.
Conduct deep data analysis to uncover insights, validate hypotheses, and guide feature engineering for improved model performance.
Partner closely with data scientists, software engineers, and business stakeholders to define problem statements, technical requirements, and deliver integrated AI/ML solutions.
Champion best practices in software engineering and MLOps to ensure quality, maintainability, and scalability of machine learning systems.
Continuously evaluate and stay current with the latest advancements in machine learning and Generative AI.
Should Have:
Master’s or PhD in Computer Science, Data Science, Statistics, or a related field.
Specific experience with GCP services such as Vertex AI, BigQuery, Google Cloud Storage, and GKE.
Experience building RAG systems from the ground up.
Strong leadership skills with the ability to mentor engineers and lead technical initiatives.
Skills:
Python.
SQL.
Data Analysis.
Machine Learning.
Generative AI.
Prompt Engineering.
Large Language Models.
Google Cloud Platform (GCP).
Vertex AI.
BigQuery.
Google Cloud Storage.
GKE.
MLOps.
CI/CD.
PyTorch.
scikit-learn.
Pandas.
Retrieval-Augmented Generation (RAG).
Qualification and Education:
Bachelor’s degree in Computer Science, Data Science, Statistics, or a related quantitative field.
Master’s or PhD in a relevant field is preferred.