AI Engineer (Gemini Enterprise)

Techolution · Remote

Source and collection record

Last observed by our collector: 9 October 2026 at 12:24 am IST. This is not a manual review or a confirmation from the employer.

Employer requisition: 256edebd-1f2a-4c10-a38e-8c732176e520

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Collected role description

Are you ready to redefine enterprise AI? Techolution, a Google Cloud Premier Partner and leading Gemini Enterprise implementation specialist, is seeking a visionary Mid-Level AI Engineer (Gemini Enterprise) to build production-grade AI systems directly within our clients' stacks. This isn't about following playbooks; it's about pioneering solutions where no precedents exist, embedding with clients from day one to architect and deliver Gemini Enterprise at production scale. Join us to drive tangible business outcomes and shape the future of AI. Apply advanced Prompt Engineering techniques for optimal model performance. Execute robust Data Preprocessing and Feature Engineering for diverse datasets. Perform Fine-tuning Large Language Models (LLMs) to meet specific project requirements. Develop and implement AI solutions leveraging cloud-based platforms. Build and deploy automated workflows for AI model lifecycle management. Integrate AI models into existing enterprise systems effectively. Design and optimize scalable AI architectures for business automation. Demonstrated expertise in Python Programming for developing robust AI applications. Solid understanding of Machine Learning & Deep Learning Fundamentals to build intelligent systems. Proficiency in Generative AI Architectures (LLMs, Transformers) for cutting-edge solutions. Experience with Google Cloud Platform (GCP) - Vertex AI for scalable AI development. A strong sense of Ownership in driving projects from conception to completion. Possesses a Seeker Mindset , continuously exploring new technologies and solutions. Exhibits Unbeatable Work Ethics , committed to delivering high-quality results consistently. Experience with Gemini API Integration & Customization is highly beneficial. Familiarity with advanced MLOps Practices on GCP , including model monitoring, is a plus. Knowledge of Containerization (Docker, Kubernetes) for ML deployments is advantageous.

Collected experience requirement

Mid-Level