AI Engineer (Gemini Enterprise)

Role overview

AI Engineer (Gemini Enterprise) at Techolution

Responsibilities

• Execute robust Data Preprocessing and Feature Engineering for diverse datasets.
• Develop and implement AI solutions leveraging cloud-based platforms.
• Build and deploy automated workflows for AI model lifecycle management.
• Design and optimize scalable AI architectures for business automation.

Employment details

• Location: Remote [India]

About the employer

• 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.

Additional role details

• Are you ready to redefine enterprise AI?
• 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.
• Perform Fine-tuning Large Language Models (LLMs) to meet specific project requirements.
• Integrate AI models into existing enterprise systems effectively.
• 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.