Senior AI Platform Engineer
Tasks
- Build MLOps and AI Ops CI CD automation
- Build maintain AI platform capabilities
- Collaborate define AI use cases technical requirements roadmaps
- Create reusable AI services APIs SDKs
- Design deploy scalable AI solutions
- Develop AI agent orchestration
- Develop optimize RAG pipelines
- Drive prompt engineering model evaluation guardrails responsible AI
- Ensure security scalability resilience compliance cost efficiency
- Implement observability evaluation monitoring
- Integrate LLM providers
- Maintain governance and responsible AI best practices
- Mentor engineers
Perks/Benefits
- Career coaching
- Career development programmes
- Certifications support
- Coaching
- Employee assistance programme
- Flexible working hours
- Global career opportunities
- Hybrid work
- Internal tech communities
- Relocation support
- Training
- Wellbeing apps
- Wellbeing programme
- Workshops conferences
Skills/Tech-stack
AI Ops | API Development | AWS | Artificial Intelligence | Azure | CI/CD | Cloud Computing | Cloud platform | Distributed Systems | Docker | Embeddings | Evaluation | Event Driven | Event-driven architecture | Google Cloud | Google Cloud Platform | Infrastructure as Code | Kubernetes | Langchain | Langgraph | Language Models | Large Language Models | MLOps | Machine Learning | Microservices | Model Governance | Monitoring | Observability | Prompt engineering | Python | RAG | Responsible AI | Retrieval-Augmented Generation | Security | Semantic Kernel | Vector Databases | Vector Search | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Regions
Countries
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