About the role
AI summarisedJoin Accenture's Data & AI practice as an AI Engineer to develop Gemini-powered applications and enterprise AI systems. You will work with Google's most capable multimodal LLMs, building production-ready solutions across the full AI lifecycle from design to deployment.
BusinessFull-timeOther Functions
Key Responsibilities
- Develop applications using Google Gemini LLMs, Vertex AI, and Google Cloud AI services
- Apply Gemini models (Pro, Flash, Ultra) for text, code, image, and multimodal enterprise solutions
- Build RAG pipelines with Gemini embeddings, long context windows, and vector search
- Implement Gemini Function Calling and Agentic AI for autonomous task execution
- Design scalable AI architectures and integrate into enterprise workflows
- Optimize model performance through prompt engineering, grounding, and safety filters
- Provide technical guidance and mentor teams adopting Google Cloud AI solutions
Requirements
- AI/ML: Proficiency in Google Gemini LLMs and Vertex AI; deep learning frameworks (TensorFlow, PyTorch)
- Programming: Python; familiarity with Java or Go
- Cloud: Google Cloud Platform (Vertex AI, Cloud Run, GKE, BigQuery)
- Gemini-specific: Function calling, system instructions, JSON mode, grounding, safety filters
- NLP: Multimodal understanding (text, image, video)
- Engineering: Scalable AI applications; CI/CD pipelines for ML
- Data: Data pipelines, preprocessing, storage (BigQuery, Cloud Storage)
- Experience with Gemini 1.5 Pro/Flash (long context, multimodal reasoning)
- MLOps and model monitoring on Vertex AI
- LangChain/LlamaIndex integration with Gemini
- Enterprise RAG architectures
- Google Cloud certifications (Professional ML Engineer, Cloud Architect)
- Client-facing or consulting experience
- Responsible AI and Gemini safety filters