Artificial Intelligence Engineer

Job Title: AI Engineer (Gemini Experience) Location: Remote Duration: Long Term Contract About the Role We are seeking a skilled AI Engineer with hands-on experience using Google Gemini (formerly Bard / PaLM 2) to design, develop, and deploy intelligent systems and applications. The ideal candidate will have a strong background in machine learning, large language models (LLMs), and generative AI, and will be responsible for building solutions that leverage Gemini APIs and Google Cloud AI ecosystem to deliver cutting-edge capabilities. Key Responsibilities • Design, develop, and implement AI models and pipelines leveraging Google Gemini models and APIs. • Integrate Gemini-powered capabilities (text, image, multimodal, or code) into business applications and workflows. • Fine-tune and evaluate LLMs for specific tasks such as summarization, conversational AI, content generation, or data extraction. • Collaborate with cross-functional teams (product, data, and engineering) to identify AI-driven opportunities and deliver proof-of-concepts (POCs). • Optimize model performance, latency, and scalability for production environments. • Stay up to date with the latest developments in Google Cloud AI, Vertex AI, and generative AI frameworks. • Maintain documentation, monitor AI system performance, and implement continuous improvements. Required Qualifications • Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field. • 3+ years of experience in AI/ML engineering or applied data science. • Proven hands-on experience using Google Gemini / Vertex AI / PaLM APIs. • Strong programming skills in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or JAX. • Experience with prompt engineering, model fine-tuning, and API integration. • Knowledge of Google Cloud Platform (GCP) services including BigQuery, Cloud Functions, and AI Studio. • Strong understanding of LLM architectures, embeddings, and generative AI workflows. Preferred Qualifications • Experience building multimodal applications (text, image, audio, or video). • Familiarity with other LLM ecosystems (OpenAI, Anthropic, Mistral, etc.) for cross-model integration. • Experience in deploying AI models into production using CI/CD pipelines and containerization (Docker/Kubernetes). • Contributions to open-source AI projects or research in LLMs or generative AI.