Data Science & GenAI Developer (GCP + Gemini)

Role Overview We are seeking highly skilled Data Science & GenAI Developers. In this role, you will be instrumental in designing, building, and deploying advanced Generative AI solutions and machine learning models leveraging the Google Cloud Platform (GCP) ecosystem. The ideal candidates will have deep hands-on experience with Google's Gemini models, Vertex AI, and end-to-end data science workflows. You will collaborate closely with our internal engineering and product teams to translate complex business problems into scalable AI-driven applications.

Key Responsibilities

• GenAI Application Development: Design, build, and deploy Generative AI applications using Google’s latest Gemini models via Vertex AI.
• RAG Architecture Implementation: Develop robust Retrieval-Augmented Generation (RAG) pipelines, integrating proprietary enterprise data with Gemini to ground model responses and reduce hallucinations.
• Data Science & Machine Learning: Perform exploratory data analysis (EDA), feature engineering, and traditional machine learning model development to support and complement GenAI initiatives. Utilize GCP services effectively, including Vertex AI, BigQuery, Cloud Storage, Cloud Functions, and Cloud Run, to build secure and scalable AI pipelines.
• Prompt Engineering & Optimization: Iteratively design, test, and refine prompts to optimize the performance, accuracy, and safety of Gemini model outputs.
• Model Deployment & MLOps: Containerize applications and deploy models into production environments, ensuring proper monitoring, logging, and performance tracking.
• Cross-Functional GCP Ecosystem Integration: Collaboration: Work alongside data engineers, software developers, and business stakeholders to define requirements and deliver impactful AI solutions.

Required Skills & Qualifications

• Experience: 3+ years of professional experience in Data Science, Machine Learning, or AI Development.
• Programming Languages: Exceptional proficiency in Python and strong SQL skills.
• Google Cloud Platform (GCP): Hands-on experience with GCP infrastructure, specifically Vertex AI, BigQuery, and deployment services (Cloud Run/GKE).
• Generative AI & LLMs: Proven experience working with Large Language Models, with a strong mandatory focus on the Google Gemini API (Pro, Flash, etc.) and Vertex AI Studio.
• AI Frameworks: Familiarity with orchestration frameworks like LangChain or LlamaIndex for building complex LLM applications.
• Vector Databases: Experience working with vector search solutions (e.g., Vertex AI Vector Search, Pinecone, or pgvector).
• Data Science Toolkit: Proficiency with standard Python libraries (Pandas, NumPy, Scikit-Learn, TensorFlow/PyTorch).
• Software Engineering Best Practices: Experience with Git, CI/CD pipelines, and containerization (Docker).

Preferred Qualifications

• Experience fine-tuning foundational models on custom datasets.
• Familiarity with data governance, AI security, and responsible AI practices within an enterprise environment.