AI Engineer - Manager - AI GTM Engine Room
Tasks
- Apply infrastructure as code
- Apply tool and function calling patterns
- Architect and implement LLM-powered applications
- Deploy and optimize AI workloads on AWS Azure and GCP
- Design and optimize data pipelines for structured and unstructured sources
- Design data driven architectures for cloud and hybrid environments
- Engineer scalable AI systems with responsible AI controls
- Establish MLOps and LLMOps practices
- Evaluate emerging models and orchestration approaches
- Implement CI/CD pipelines
- Implement agentic workflows with multi agent orchestration
- Implement experimentation tracking for MLflow and Weights Biases
- Lead prototyping and deployment of AI and Generative AI solutions
- Mentor and guide junior engineers
- Monitor models and enable safe deployments
- Select and implement vector database strategies
- Use containerisation for AI deployments
Perks/Benefits
- Career development
- Comprehensive benefits package
- Flex leave
- Flexible work arrangements
- Paid parental leave
Skills/Tech-stack
AWS | Agent Orchestration | Agentic Workflows | Azure | CI/CD | Cloud platform | Containerisation | Data Engineering | Data Pipelines | Generative AI | Google Cloud | Google Cloud Platform | Governance | Infrastructure as Code | LLM-powered Applications | LLMOps | Langchain | Language Models | Large Language Models | MLOps | MLflow | Machine Learning | Multi-Agent | Multi-agent orchestration | NoSQL | Python | Relational databases | Responsible AI | Security | Vector Databases | Weights & Biases | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Engineer | AI Engineering | AI Engineering Manager | Engineer | Engineering Manager | Manager
Regions
Countries
States
Cities
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