Job Description

We are seeking a highly skilled and innovative Data Scientist with hands-on experience in Generative AI (GenAI), Gemini, and cloud migration. The ideal candidate will have a strong foundation in Python programming, database management, and experience deploying solutions from on-premises infrastructure to the cloud (preferably Google Cloud Platform, but AWS or Azure are acceptable). This role involves developing intelligent data-driven solutions, building and deploying ML/GenAI models, and modernizing legacy systems by transitioning them to the cloud.

Key Responsibilities

Design, develop, and implement advanced analytics and Generative AI models using tools such as Gemini and other LLM frameworks. Build scalable data science workflows and pipelines in Python. Deploy, monitor, and maintain machine learning and AI solutions in cloud environments (preferably GCP, but AWS or Azure acceptable). Lead the migration of models, data pipelines, and analytics platforms from on-prem environments to cloud infrastructure. Collaborate with cross-functional teams to integrate AI/ML models into production systems. Manage relational and non-relational databases, including optimization and data integration. Ensure best practices in model versioning, reproducibility, testing, and deployment (CI/CD). Document data science solutions and communicate technical findings to non-technical stakeholders.

Required Qualifications

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field. 3+ years of professional experience in data science or machine learning roles. Proven experience with Generative AI frameworks, including Gemini, GPT-based models, or similar technologies. Proficiency in Python for data manipulation, machine learning, and system automation. Strong experience deploying AI/ML models and data solutions to cloud platforms (GCP preferred; AWS/Azure acceptable). Solid experience with on-premises environments and migrating code/solutions to the cloud. Expertise in SQL and NoSQL database design, management, and performance tuning. Experience with version control (e.g., Git), containerization (e.g., Docker), and orchestration tools (e.g., Kubernetes) is a plus. Strong problem-solving skills and ability to work independently and in teams.

Preferred Qualifications

GCP certifications (e.g., Professional Data Engineer or ML Engineer) or equivalent in AWS/Azure. Familiarity with MLOps and DevOps workflows. Experience working with BigQuery, Vertex AI, or other cloud-native AI/ML tools. Strong understanding of data governance, security, and compliance in cloud environments.
Job Type: Fixed term contract

Schedule:

Monday to Friday
Work Location: In person